machine-learning.md

1. a3c
2. actor-critic
3. ai
4. algorithm
5. algorithms
6. analytics
7. apache-spark
8. apple
9. arkit
10. artificial-intelligence
11. augmentation
12. autoencoder
13. automated-machine-learning
14. automatic-differentiation
15. automation
16. automl
17. awesome
18. awesome-list
19. aws
20. azure
21. big-data
22. bioinformatics
23. book
24. bot
25. bot-framework
26. botkit
27. bots
28. c-plus-plus
29. c-sharp
30. caffe
31. caffe2
32. calculus
33. chatbot
34. classification
35. cloud
36. clustering
37. cnn
38. cntk
39. collaborative-filtering
40. computer-science
41. computer-vision
42. conversational-agents
43. conversational-ai
44. conversational-bots
45. convolutional-neural-networks
46. core-ml
47. coreml
48. cpp
49. cuda
50. cython
51. darknet
52. data
53. data-analysis
54. data-mining
55. data-science
56. data-scientists
57. data-visualization
58. database
59. dataset
60. decision-trees
61. deep-learning-tutorial
62. deep-neural-network
63. deep-neural-networks
64. deep-q-network
65. deep-reinforcement-learning
66. deeplearning
67. distributed
68. distributed-computing
69. distributed-systems
70. docker
71. domain-specific-language
72. dqn
73. dropout
74. elasticsearch
75. embeddings
76. ensemble-learning
77. examples
78. face-recognition
79. facebook
80. faster-rcnn
81. fasttext
82. feature-engineering
83. finance
84. flappy-bird
85. framework
86. gamedev
87. gan
88. gans
89. gbdt
90. gbm
91. generative-adversarial-network
92. generative-adversarial-networks
93. generative-model
94. genetic-algorithm
95. genetic-programming
96. gensim
97. go
98. golang
99. gpu
100. gpu-acceleration
101. gradient-boosting
102. graph
103. h2o
104. hadoop
105. high-performance-computing
106. hyperparameter-optimization
107. image-classification
108. image-processing
109. image-recognition
110. imagenet
111. information-extraction
112. ios
113. ios11
114. iot
115. ipynb
116. ipython
117. ipython-notebook
118. java
119. javascript
120. jupyter
121. jupyter-notebook
122. kafka
123. kaggle
124. kaggle-competition
125. keras
126. kubernetes
127. language
128. learning
129. learning-to-rank
130. lightgbm
131. linear-algebra
132. linear-regression
133. linux
134. list
135. logistic-regression
136. lstm
137. lua
138. machine
139. machine-intelligence
140. machinelearning
141. macos
142. matplotlib
143. matrix
144. matrix-factorization
145. mcmc
146. metal
147. microservices
148. microsoft
149. ml
150. mnist
151. model
152. model-selection
153. mongodb
154. mooc
155. multi-threading
156. music
157. mxnet
158. naive-bayes
159. named-entity-recognition
160. natural-language-processing
161. neural-network
162. neural-networks
163. neuroevolution
164. nlp
165. nlp-library
166. nlu
167. notebook
168. numpy
169. object-detection
170. ocr
171. opencv
172. opensource
173. optimization
174. pandas
175. papers
176. parallel
177. pipeline
178. plotting
179. policy-gradient
180. prediction
181. predictive-modeling
182. probabilistic-programming
183. procedural-generation
184. programming
185. programming-language
186. pyspark
187. python-library
188. pytorch
189. pytorch-tutorial
190. pytorch-tutorials
191. quant
192. quantitative-trading
193. r
194. r-package
195. random-forest
196. rasa
197. rbm
198. react
199. real-time
200. recommender-system
201. recurrent-neural-networks
202. regression
203. reinforcement-learning
204. reproducibility
205. restricted-boltzmann-machine
206. rnn
207. robotics
208. ruby
209. rubyml
210. rubynlp
211. rust
212. scala
213. science
214. scientific-computing
215. scikit-learn
216. scipy
217. security
218. self-driving-car
219. sentiment-analysis
220. spacy
221. spark
222. speech-recognition
223. speech-to-text
224. stacking
225. stanford
226. statistics
227. stock-market
228. supervised-learning
229. support-vector-machines
230. svm
231. swift
232. tensorboard
233. tensorflow-models
234. tensorflow-tutorials
235. tensorlayer
236. text-classification
237. text-mining
238. tflearn
239. theano
240. topic-modeling
241. torch
242. tutorial
243. typescript
244. unsupervised-learning
245. variational-inference
246. visualization
247. visualizer
248. webgl
249. word-embeddings
250. word-vectors
251. word2vec
252. xgboost

a3c

1. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
2. reinforcement-learning. Minimal and Clean Reinforcement Learning Examples
3. Reinforcement-learning-with-tensorflow. Simple Reinforcement learning tutorials

actor-critic

1. reinforcement-learning. Minimal and Clean Reinforcement Learning Examples
2. Reinforcement-learning-with-tensorflow. Simple Reinforcement learning tutorials

ai

1. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
2. gun. A realtime, decentralized, offline-first, graph database engine.
3. caffe2. Caffe2 is a lightweight, modular, and scalable deep learning framework.
4. EmojiIntelligence. Neural Network built in Apple Playground using Swift
5. snorkel. A system for quickly generating training data with weak supervision
6. Netron. Visualizer for deep learning and machine learning models
7. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
8. thinc. 🔮 spaCy's Machine Learning library for NLP in Python
9. deep-neuroevolution. Deep Neuroevolution

algorithm

1. WaveFunctionCollapse. Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics.
2. AlgoWiki. Repository which contains links and resources on different topics of Computer Science.
3. machine_learning_basics. Plain python implementations of basic machine learning algorithms
4. SynTex. Texture synthesis from examples.

algorithms

1. cs-video-courses. List of Computer Science courses with video lectures.
2. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian

analytics

1. awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems.
2. weld. High-performance runtime for data analytics applications
3. auto_ml. Automated machine learning for analytics & production
4. papers-I-read. A-Paper-A-Week
5. sciblog_support. Support content for my blog

apache-spark

1. oryx. Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning
2. sparkit-learn. PySpark + Scikit-learn = Sparkit-learn
3. sparklyr. R interface for Apache Spark

apple

1. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
2. Bender. Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
3. EmojiIntelligence. Neural Network built in Apple Playground using Swift

arkit

1. CoreML-in-ARKit. Simple project to detect objects and display 3D labels above them in AR. This serves as a basic template for an ARKit project to use CoreML.
2. FaceRecognition-in-ARKit. Detects faces using the Vision-API and runs the extracted face through a CoreML-model to identiy the specific persons.

artificial-intelligence

1. machine-learning-for-software-engineers. A complete daily plan for studying to become a machine learning engineer.
2. incubator-mxnet. Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
3. cheatsheets-ai. Essential Cheat Sheets for deep learning and machine learning researchers
4. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
5. gun. A realtime, decentralized, offline-first, graph database engine.
6. caffe2. Caffe2 is a lightweight, modular, and scalable deep learning framework.
7. php-ml. PHP-ML - Machine Learning library for PHP
8. Swift-AI. The Swift machine learning library.
9. SerpentAI. Game Agent Framework. Helping you create AIs / Bots to play any game you own!
10. EasyPR. An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations.
11. awesome-artificial-intelligence. A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers
12. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
13. serenata-de-amor. 🕵 Artificial Intelligence for social control of public administration
14. AlgoWiki. Repository which contains links and resources on different topics of Computer Science.
15. pipeline. PipelineAI: Real-Time Enterprise AI Platform
16. shogun. Shōgun
17. deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
18. gorgonia. Gorgonia is a library that helps facilitate machine learning in Go.
19. gophernotes. The Go kernel for Jupyter notebooks and nteract.
20. AI-Blocks. A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models!
21. EmojiIntelligence. Neural Network built in Apple Playground using Swift
22. iOS_ML. List of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.
23. gosl. Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more.
24. polyaxon. An open source platform for reproducible machine learning at scale
25. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
26. HyperGAN. A composable Generative Adversarial Network(GAN) with API and command line tool.
27. auto_ml. Automated machine learning for analytics & production
28. DoYouEvenLearn. Essential Guide to keep up with AI/ML/CV/UNameIt
29. devol. Automated deep neural network design via genetic algorithms
30. DeepAudioClassification. Finding the genre of a song with Deep Learning
31. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
32. high-school-guide-to-machine-learning. Being a high schooler myself and having studied Machine Learning and Artificial Intelligence for a year now, I believe that there fails to exist a learning path in this field for High School students. This is my attempt at creating one.
33. papers-I-read. A-Paper-A-Week
34. thinc. 🔮 spaCy's Machine Learning library for NLP in Python
35. lycheejs. 🌱 Next-Gen AI-Assisted Isomorphic Application Engine for Embedded, Console, Mobile, Server and Desktop
36. Dragonfire. Dragonfire is an open-source virtual assistant for Ubuntu based Linux distributions
37. ai-deadlines. ⏰ AI conference deadline countdowns
38. sciblog_support. Support content for my blog

augmentation

1. imgaug. Image augmentation for machine learning experiments.
2. Augmentor. Image augmentation library in Python for machine learning.

autoencoder

1. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
2. deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
3. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
4. PyTorch-Tutorial. Build your neural network easy and fast
5. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials

automated-machine-learning

1. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
2. xcessiv. A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
3. auto_ml. Automated machine learning for analytics & production
4. MLBox. MLBox is a powerful Automated Machine Learning python library.

automatic-differentiation

1. gorgonia. Gorgonia is a library that helps facilitate machine learning in Go.
2. tangent. Source-to-Source Debuggable Derivatives in Pure Python
3. DeepLearning.scala. A simple library for creating complex neural networks
4. owl. Owl is an OCaml library for scientific and engineering computing.

automation

1. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
2. datacleaner. A Python tool that automatically cleans data sets and readies them for analysis.

automl

1. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
2. auto_ml. Automated machine learning for analytics & production
3. MLBox. MLBox is a powerful Automated Machine Learning python library.

awesome

1. Qix. Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang
2. awesome-deep-learning. A curated list of awesome Deep Learning tutorials, projects and communities.
3. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
4. awesome-nlp. 📖 A curated list of resources dedicated to Natural Language Processing (NLP)
5. useful-java-links. A list of useful Java frameworks, libraries, software and hello worlds examples
6. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
7. awesome-machine-learning-on-source-code. Interesting links & research papers related to Machine Learning applied to source code
8. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
9. awesome-awesome. A curated list of awesome curated lists of many topics.
10. machine-learning-with-ruby. Curated list: Resources for machine learning in Ruby.
11. machine-learning-surveys. A curated list of Machine Learning Surveys, Tutorials and Books.
12. Awesome-Deep-Learning-Resources. Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
13. nlp-with-ruby. Practical Natural Language Processing done in Ruby.
14. Awesome-TensorFlow-Chinese. Awesome-TensorFlow-Chinese，TensorFlow 中文资源精选，官方网站，安装教程，入门教程，视频教程，实战项目，学习路径。QQ群：522785813，微信群二维码：http://www.tensorflownews.com/
15. awesome-project-ideas. Curated list of Machine Learning, NLP, Vision Project Ideas
16. awesome-embedding-models. A curated list of awesome embedding models tutorials, projects and communities.

awesome-list

1. awesome-deep-learning. A curated list of awesome Deep Learning tutorials, projects and communities.
2. awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems.
3. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
4. awesome-nlp. 📖 A curated list of resources dedicated to Natural Language Processing (NLP)
5. useful-java-links. A list of useful Java frameworks, libraries, software and hello worlds examples
6. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
7. awesome-machine-learning-on-source-code. Interesting links & research papers related to Machine Learning applied to source code
8. awesome-ml-for-cybersecurity. Machine Learning for Cyber Security
9. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
10. awesome-awesome. A curated list of awesome curated lists of many topics.
11. iOS_ML. List of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.
12. machine-learning-with-ruby. Curated list: Resources for machine learning in Ruby.
13. machine-learning-surveys. A curated list of Machine Learning Surveys, Tutorials and Books.
14. Awesome-Deep-Learning-Resources. Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
15. nlp-with-ruby. Practical Natural Language Processing done in Ruby.
16. awesome-project-ideas. Curated list of Machine Learning, NLP, Vision Project Ideas

aws

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. awesome-aws. A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.
3. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes
4. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

azure

1. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes
2. mmlspark. Microsoft Machine Learning for Apache Spark

big-data

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
3. gun. A realtime, decentralized, offline-first, graph database engine.
4. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
5. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
6. datumbox-framework. Datumbox is an open-source Machine Learning framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
7. spark-py-notebooks. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
8. sciblog_support. Support content for my blog

bioinformatics

1. cs-video-courses. List of Computer Science courses with video lectures.
2. deepvariant. DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

book

1. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
2. mit-deep-learning-book-pdf. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

bot

1. rasa_nlu. turn natural language into structured data
2. telegram-list. List of telegram groups, channels & bots // Список интересных групп, каналов и ботов телеграма // Список чатов для программистов
3. rasa_core. machine learning based dialogue engine for conversational software

bot-framework

1. rasa_nlu. turn natural language into structured data
2. rasa_core. machine learning based dialogue engine for conversational software

botkit

1. rasa_nlu. turn natural language into structured data
2. rasa_core. machine learning based dialogue engine for conversational software

bots

1. rasa_nlu. turn natural language into structured data
2. rasa_core. machine learning based dialogue engine for conversational software

c-plus-plus

1. CNTK. Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
2. dlib. A toolkit for making real world machine learning and data analysis applications in C++
3. tiny-dnn. header only, dependency-free deep learning framework in C++14
4. mlpack. mlpack: a scalable C++ machine learning library --
5. shogun. Shōgun
6. nmap. Nmap - the Network Mapper. Github mirror of official SVN repository.
7. MITIE. MITIE: library and tools for information extraction
8. jubatus. Framework and Library for Distributed Online Machine Learning
9. root. The official ROOT repository
10. eos. A lightweight 3D Morphable Face Model fitting library in modern C++11/14

c-sharp

1. CNTK. Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
2. WaveFunctionCollapse. Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics.
3. framework. Machine learning, computer vision, statistics and general scientific computing for .NET
4. TensorFlowSharp. TensorFlow API for .NET languages
5. SynTex. Texture synthesis from examples.

caffe

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. openpose. OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation
3. have-fun-with-machine-learning. An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
4. DIGITS. Deep Learning GPU Training System
5. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
6. deepdetect. Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow, XGBoost and TSNE
7. polyaxon. An open source platform for reproducible machine learning at scale
8. turkce-yapay-zeka-kaynaklari. Türkiye'de yapılan derin öğrenme (deep learning) ve makine öğrenmesi (machine learning) çalışmalarının derlendiği sayfa.
9. Netron. Visualizer for deep learning and machine learning models

caffe2

1. caffe2. Caffe2 is a lightweight, modular, and scalable deep learning framework.
2. Netron. Visualizer for deep learning and machine learning models

calculus

1. deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
2. hackermath. Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way

chatbot

1. stanford-tensorflow-tutorials. This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
2. ChatterBot. ChatterBot is a machine learning, conversational dialog engine for creating chat bots
3. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
4. rasa_nlu. turn natural language into structured data
5. rasa_core. machine learning based dialogue engine for conversational software
6. Dragonfire. Dragonfire is an open-source virtual assistant for Ubuntu based Linux distributions

classification

1. php-ml. PHP-ML - Machine Learning library for PHP
2. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
3. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
4. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
5. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
6. PyTorch-Tutorial. Build your neural network easy and fast
7. mlr. mlr: Machine Learning in R
8. HyperGAN. A composable Generative Adversarial Network(GAN) with API and command line tool.
9. awesome-project-ideas. Curated list of Machine Learning, NLP, Vision Project Ideas
10. MLBox. MLBox is a powerful Automated Machine Learning python library.

cloud

1. awesome-aws. A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.
2. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes

clustering

1. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
2. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
3. DAT8. General Assembly's Data Science course in Washington, DC
4. mlr. mlr: Machine Learning in R
5. hdbscan. A high performance implementation of HDBSCAN clustering.

cnn

1. BossSensor. Hide screen when boss is approaching.
2. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
3. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
4. tensorflow_template_application. TensorFlow template application for deep learning
5. PyTorch-Tutorial. Build your neural network easy and fast
6. ChineseIDCardOCR. [Deprecated] 🇨🇳中国二代身份证光学识别
7. Awesome-Deep-Learning-Resources. Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
8. one-pixel-attack-keras. Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on cifar10
9. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials
10. DLTK. Deep Learning Toolkit for Medical Image Analysis

cntk

1. CNTK. Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
2. mmlspark. Microsoft Machine Learning for Apache Spark

collaborative-filtering

1. Deep-Learning-for-Recommendation-Systems. This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
2. implicit. Fast Python Collaborative Filtering for Implicit Datasets

computer-science

1. cs-video-courses. List of Computer Science courses with video lectures.
2. AlgoWiki. Repository which contains links and resources on different topics of Computer Science.
3. papers-I-read. A-Paper-A-Week

computer-vision

1. cs-video-courses. List of Computer Science courses with video lectures.
2. openpose. OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation
3. BossSensor. Hide screen when boss is approaching.
4. dlib. A toolkit for making real world machine learning and data analysis applications in C++
5. SerpentAI. Game Agent Framework. Helping you create AIs / Bots to play any game you own!
6. TensorFlow-World. 🌎 Simple and ready-to-use tutorials for TensorFlow
7. EasyPR. An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations.
8. fashion-mnist. A MNIST-like fashion product database. Benchmark 👉
9. framework. Machine learning, computer vision, statistics and general scientific computing for .NET
10. Neural-Photo-Editor. A simple interface for editing natural photos with generative neural networks.
11. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
12. luminoth. Deep Learning toolkit for Computer Vision
13. vision. Datasets, Transforms and Models specific to Computer Vision
14. arXivTimes. repository to research & share the machine learning articles
15. handong1587.github.io.
16. iOS_ML. List of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.
17. papers. Summaries of machine learning papers
18. vehicle-detection. Vehicle detection using machine learning and computer vision techniques for Udacity's self-driving car course.
19. AlphaPose. Multi-Person Pose Estimation System
20. ComputeLibrary. The ARM Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies.
21. ssd.pytorch. A PyTorch Implementation of Single Shot MultiBox Detector
22. crnn. Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition.
23. DoYouEvenLearn. Essential Guide to keep up with AI/ML/CV/UNameIt
24. eos. A lightweight 3D Morphable Face Model fitting library in modern C++11/14
25. ai-deadlines. ⏰ AI conference deadline countdowns

conversational-agents

1. rasa_nlu. turn natural language into structured data
2. rasa_core. machine learning based dialogue engine for conversational software

conversational-ai

1. rasa_nlu. turn natural language into structured data
2. rasa_core. machine learning based dialogue engine for conversational software

conversational-bots

1. rasa_nlu. turn natural language into structured data
2. rasa_core. machine learning based dialogue engine for conversational software

convolutional-neural-networks

1. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
2. fashion-mnist. A MNIST-like fashion product database. Benchmark 👉
3. darkflow. Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
4. deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
5. Neural-Photo-Editor. A simple interface for editing natural photos with generative neural networks.
6. Bender. Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
7. grenade. Deep Learning in Haskell
8. turkce-yapay-zeka-kaynaklari. Türkiye'de yapılan derin öğrenme (deep learning) ve makine öğrenmesi (machine learning) çalışmalarının derlendiği sayfa.
9. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
10. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials
11. NeuralKart. A Real-time Mario Kart AI using CNNs, Offline Search, and DAGGER
12. FaceRank. FaceRank - Rank Face by CNN Model based on TensorFlow (add keras version). FaceRank-人脸打分基于 TensorFlow (新增 Keras 版本) 的 CNN 模型（QQ群：522785813）。技术支持：http://tensorflow123.com

core-ml

1. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
2. MobileNet-CoreML. The MobileNet neural network using Apple's new CoreML framework

coreml

1. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
2. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
3. CoreML-in-ARKit. Simple project to detect objects and display 3D labels above them in AR. This serves as a basic template for an ARKit project to use CoreML.
4. ChineseIDCardOCR. [Deprecated] 🇨🇳中国二代身份证光学识别
5. Netron. Visualizer for deep learning and machine learning models
6. FaceRecognition-in-ARKit. Detects faces using the Vision-API and runs the extracted face through a CoreML-model to identiy the specific persons.

cpp

1. openpose. OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation
2. tensorflow_template_application. TensorFlow template application for deep learning
3. awesome-quant. 中国的Quant相关资源索引
4. ComputeLibrary. The ARM Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies.

cuda

1. chainer. A flexible framework of neural networks for deep learning
2. Deep-Learning-Boot-Camp. A community run, 5-day PyTorch Deep Learning Bootcamp

cython

1. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
2. pomegranate. Fast, flexible and easy to use probabilistic modelling in Python.

darknet

1. darkflow. Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
2. yolo-9000. YOLO9000: Better, Faster, Stronger - Real-Time Object Detection. 9000 classes!

data

1. machine-learning-mindmap. A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
2. awesome-awesome. A curated list of awesome curated lists of many topics.
3. weld. High-performance runtime for data analytics applications

data-analysis

1. scikit-learn. scikit-learn: machine learning in Python
2. Data-Analysis-and-Machine-Learning-Projects. Repository of teaching materials, code, and data for my data analysis and machine learning projects.
3. imbalanced-learn. Python module to perform under sampling and over sampling with various techniques.
4. xlearn. High Performance, Easy-to-use, and Scalable Machine Learning Package (C++, Python, R)
5. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian
6. DAT8. General Assembly's Data Science course in Washington, DC
7. spark-py-notebooks. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

data-mining

1. python-machine-learning-book. The "Python Machine Learning (1st edition)" book code repository and info resource
2. ML-From-Scratch. Machine Learning From Scratch. Bare bones Python implementations of Machine Learning models and algorithms with a focus on transparency and accessibility. Aims to cover everything from Data Mining to Deep Learning.
3. awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems.
4. gensim. Topic Modelling for Humans
5. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
6. awesome-ml-for-cybersecurity. Machine Learning for Cyber Security
7. mlxtend. A library of extension and helper modules for Python's data analysis and machine learning libraries.
8. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
9. featuretools. automated feature engineering
10. vvedenie-mashinnoe-obuchenie. 📝 Подборка ресурсов по машинному обучению

data-science

1. keras. Deep Learning for humans
2. scikit-learn. scikit-learn: machine learning in Python
3. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
4. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
5. python-machine-learning-book. The "Python Machine Learning (1st edition)" book code repository and info resource
6. ML-From-Scratch. Machine Learning From Scratch. Bare bones Python implementations of Machine Learning models and algorithms with a focus on transparency and accessibility. Aims to cover everything from Data Mining to Deep Learning.
7. dive-into-machine-learning. Dive into Machine Learning with Python Jupyter notebook and scikit-learn!
8. tflearn. Deep learning library featuring a higher-level API for TensorFlow.
9. awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems.
10. gensim. Topic Modelling for Humans
11. php-ml. PHP-ML - Machine Learning library for PHP
12. data-science-blogs. A curated list of data science blogs
13. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
14. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
15. Data-Analysis-and-Machine-Learning-Projects. Repository of teaching materials, code, and data for my data analysis and machine learning projects.
16. edward. A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
17. serenata-de-amor. 🕵 Artificial Intelligence for social control of public administration
18. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
19. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
20. shogun. Shōgun
21. imbalanced-learn. Python module to perform under sampling and over sampling with various techniques.
22. deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
23. xlearn. High Performance, Easy-to-use, and Scalable Machine Learning Package (C++, Python, R)
24. scikit-learn-videos. Jupyter notebooks from the scikit-learn video series
25. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
26. benchm-ml. A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
27. mlxtend. A library of extension and helper modules for Python's data analysis and machine learning libraries.
28. machine_learning_examples. A collection of machine learning examples and tutorials.
29. snips-nlu. Snips Python library to extract meaning from text
30. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian
31. machine-learning. 🌎 machine learning algorithms tutorials (mainly in Python3)
32. gophernotes. The Go kernel for Jupyter notebooks and nteract.
33. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
34. DAT8. General Assembly's Data Science course in Washington, DC
35. telegram-list. List of telegram groups, channels & bots // Список интересных групп, каналов и ботов телеграма // Список чатов для программистов
36. featuretools. automated feature engineering
37. scikit-plot. An intuitive library to add plotting functionality to scikit-learn objects.
38. python-machine-learning-book-2nd-edition. The "Python Machine Learning (2nd edition)" book code repository and info resource
39. xcessiv. A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
40. polyaxon. An open source platform for reproducible machine learning at scale
41. datumbox-framework. Datumbox is an open-source Machine Learning framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
42. mlr. mlr: Machine Learning in R
43. Deep-Learning-Boot-Camp. A community run, 5-day PyTorch Deep Learning Bootcamp
44. vvedenie-mashinnoe-obuchenie. 📝 Подборка ресурсов по машинному обучению
45. auto_ml. Automated machine learning for analytics & production
46. spark-py-notebooks. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
47. datacleaner. A Python tool that automatically cleans data sets and readies them for analysis.
48. devol. Automated deep neural network design via genetic algorithms
49. eli5. A library for debugging/inspecting machine learning classifiers and explaining their predictions
50. dvc. Git for data scientists - manage your code and data together
51. h2o-tutorials. Tutorials and training material for the H2O Machine Learning Platform
52. scattertext. Beautiful visualizations of how language differs among document types.
53. keras-contrib. Keras community contributions
54. datascience-pizza. Repositório para juntar informações sobre materiais de estudo em análise de dados e áreas afins, empresas que trabalham com dados e dicionário de conceitos
55. DLTK. Deep Learning Toolkit for Medical Image Analysis
56. MLBox. MLBox is a powerful Automated Machine Learning python library.
57. sciblog_support. Support content for my blog

data-scientists

1. awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems.
2. datascience-pizza. Repositório para juntar informações sobre materiais de estudo em análise de dados e áreas afins, empresas que trabalham com dados e dicionário de conceitos

data-visualization

1. awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems.
2. facets. Visualizations for machine learning datasets
3. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
4. DAT8. General Assembly's Data Science course in Washington, DC

database

1. gun. A realtime, decentralized, offline-first, graph database engine.
2. mapd-core. The MapD Core database
3. awesome-awesome. A curated list of awesome curated lists of many topics.

dataset

1. fashion-mnist. A MNIST-like fashion product database. Benchmark 👉
2. PyTorch-NLP. Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)
3. awesome-project-ideas. Curated list of Machine Learning, NLP, Vision Project Ideas
4. FaceRank. FaceRank - Rank Face by CNN Model based on TensorFlow (add keras version). FaceRank-人脸打分基于 TensorFlow (新增 Keras 版本) 的 CNN 模型（QQ群：522785813）。技术支持：http://tensorflow123.com

decision-trees

1. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
2. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
3. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
4. DAT8. General Assembly's Data Science course in Washington, DC

deep-learning-tutorial

1. awesome-deep-learning. A curated list of awesome Deep Learning tutorials, projects and communities.
2. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
3. AndroidTensorFlowMachineLearningExample. Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
4. kur. Descriptive Deep Learning
5. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials

deep-neural-network

1. tfjs. A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
2. deepvariant. DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

deep-neural-networks

1. tensorflow. Computation using data flow graphs for scalable machine learning
2. awesome-deep-learning-papers. The most cited deep learning papers
3. CNTK. Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
4. incubator-mxnet. Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
5. caffe2. Caffe2 is a lightweight, modular, and scalable deep learning framework.
6. tfjs-core. WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
7. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
8. darkflow. Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
9. gorgonia. Gorgonia is a library that helps facilitate machine learning in Go.
10. Bender. Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
11. grenade. Deep Learning in Haskell
12. Forge. A neural network toolkit for Metal
13. AndroidTensorFlowMachineLearningExample. Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
14. zi2zi. Learning Chinese Character style with conditional GAN
15. kur. Descriptive Deep Learning
16. DeepLearning.scala. A simple library for creating complex neural networks
17. ai-deadlines. ⏰ AI conference deadline countdowns
18. DLTK. Deep Learning Toolkit for Medical Image Analysis

deep-q-network

1. reinforcement-learning. Minimal and Clean Reinforcement Learning Examples
2. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
3. Reinforcement-learning-with-tensorflow. Simple Reinforcement learning tutorials

deep-reinforcement-learning

1. ML-From-Scratch. Machine Learning From Scratch. Bare bones Python implementations of Machine Learning models and algorithms with a focus on transparency and accessibility. Aims to cover everything from Data Mining to Deep Learning.
2. reinforcement-learning. Minimal and Clean Reinforcement Learning Examples
3. papers. Summaries of machine learning papers
4. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials

deeplearning

1. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
2. mit-deep-learning-book-pdf. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
3. horovod. Distributed training framework for TensorFlow.
4. gorgonia. Gorgonia is a library that helps facilitate machine learning in Go.
5. AndroidTensorFlowMachineLearningExample. Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
6. zi2zi. Learning Chinese Character style with conditional GAN
7. auto_ml. Automated machine learning for analytics & production
8. Netron. Visualizer for deep learning and machine learning models

distributed

1. tensorflow. Computation using data flow graphs for scalable machine learning
2. CNTK. Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
3. Qix. Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang
4. handson-ml. A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
5. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
6. ray. A high-performance distributed execution engine
7. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
8. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
9. jubatus. Framework and Library for Distributed Online Machine Learning
10. MLBox. MLBox is a powerful Automated Machine Learning python library.

distributed-computing

1. Qix. Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang
2. sparkit-learn. PySpark + Scikit-learn = Sparkit-learn

distributed-systems

1. incubator-mxnet. Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
2. Qix. Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang

docker

1. pipeline. PipelineAI: Real-Time Enterprise AI Platform
2. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian
3. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes
4. HackPrincetonF16. Chrome extension to flag fake news on Facebook

domain-specific-language

1. TensorComprehensions. A domain specific language to express machine learning workloads.
2. DeepLearning.scala. A simple library for creating complex neural networks

dqn

1. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
2. reinforcement-learning. Minimal and Clean Reinforcement Learning Examples
3. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
4. Reinforcement-learning-with-tensorflow. Simple Reinforcement learning tutorials
5. PyTorch-Tutorial. Build your neural network easy and fast

dropout

1. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
2. PyTorch-Tutorial. Build your neural network easy and fast

elasticsearch

1. awesome-aws. A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.
2. pipeline. PipelineAI: Real-Time Enterprise AI Platform

embeddings

1. PyTorch-NLP. Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)
2. awesome-embedding-models. A curated list of awesome embedding models tutorials, projects and communities.

ensemble-learning

1. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
2. DAT8. General Assembly's Data Science course in Washington, DC
3. xcessiv. A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
4. gcForest. This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

examples

1. TensorFlow-Examples. TensorFlow Tutorial and Examples for Beginners with Latest APIs
2. sciblog_support. Support content for my blog

face-recognition

1. face_recognition. The world's simplest facial recognition api for Python and the command line
2. FaceRank. FaceRank - Rank Face by CNN Model based on TensorFlow (add keras version). FaceRank-人脸打分基于 TensorFlow (新增 Keras 版本) 的 CNN 模型（QQ群：522785813）。技术支持：http://tensorflow123.com
3. opencv. OpenCV projects: Face Recognition, Machine Learning, Colormaps, Local Binary Patterns, Examples...
4. FaceRecognition-in-ARKit. Detects faces using the Vision-API and runs the extracted face through a CoreML-model to identiy the specific persons.

facebook

1. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
2. HackPrincetonF16. Chrome extension to flag fake news on Facebook

faster-rcnn

1. luminoth. Deep Learning toolkit for Computer Vision
2. AlphaPose. Multi-Person Pose Estimation System

fasttext

1. gensim. Topic Modelling for Humans
2. fastText.py. A Python interface for Facebook fastText

feature-engineering

1. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
2. featuretools. automated feature engineering
3. auto_ml. Automated machine learning for analytics & production

finance

1. awesome-quant. 中国的Quant相关资源索引
2. bulbea. 🐗 🐻 Deep Learning based Python Library for Stock Market Prediction and Modelling

flappy-bird

1. FlappyBirdRL. Flappy Bird hack using Reinforcement Learning
2. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

framework

1. SerpentAI. Game Agent Framework. Helping you create AIs / Bots to play any game you own!
2. framework. Machine learning, computer vision, statistics and general scientific computing for .NET

gamedev

1. WaveFunctionCollapse. Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics.
2. SynTex. Texture synthesis from examples.

gan

1. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
2. fashion-mnist. A MNIST-like fashion product database. Benchmark 👉
3. generative-models. Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
4. the-gan-zoo. A list of all named GANs!
5. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
6. papers. Summaries of machine learning papers
7. PyTorch-Tutorial. Build your neural network easy and fast
8. HyperGAN. A composable Generative Adversarial Network(GAN) with API and command line tool.
9. pytorch-generative-adversarial-networks. A very simple generative adversarial network (GAN) in PyTorch

gans

1. Neural-Photo-Editor. A simple interface for editing natural photos with generative neural networks.
2. pytorch-generative-adversarial-networks. A very simple generative adversarial network (GAN) in PyTorch

gbdt

1. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
2. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R

gbm

1. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
2. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
3. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R

generative-adversarial-network

1. the-gan-zoo. A list of all named GANs!
2. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
3. PyTorch-Tutorial. Build your neural network easy and fast
4. HyperGAN. A composable Generative Adversarial Network(GAN) with API and command line tool.

generative-adversarial-networks

1. grenade. Deep Learning in Haskell
2. zi2zi. Learning Chinese Character style with conditional GAN

generative-model

1. generative-models. Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
2. HyperGAN. A composable Generative Adversarial Network(GAN) with API and command line tool.

genetic-algorithm

1. ML-From-Scratch. Machine Learning From Scratch. Bare bones Python implementations of Machine Learning models and algorithms with a focus on transparency and accessibility. Aims to cover everything from Data Mining to Deep Learning.
2. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
3. devol. Automated deep neural network design via genetic algorithms
4. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
5. neataptic. 🚀 Blazing fast neuro-evolution & backpropagation for the browser and Node.js

genetic-programming

1. devol. Automated deep neural network design via genetic algorithms
2. gplearn. Genetic Programming in Python, with a scikit-learn inspired API

gensim

1. gensim. Topic Modelling for Humans
2. sense2vec. 🦆 Use NLP to go beyond vanilla word2vec

go

1. Qix. Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang
2. gorgonia. Gorgonia is a library that helps facilitate machine learning in Go.
3. gophernotes. The Go kernel for Jupyter notebooks and nteract.

golang

1. gorgonia. Gorgonia is a library that helps facilitate machine learning in Go.
2. tensorflow_template_application. TensorFlow template application for deep learning
3. gophernotes. The Go kernel for Jupyter notebooks and nteract.

gpu

1. chainer. A flexible framework of neural networks for deep learning
2. DIGITS. Deep Learning GPU Training System
3. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
4. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
5. pipeline. PipelineAI: Real-Time Enterprise AI Platform
6. mapd-core. The MapD Core database
7. Deep-Learning-Boot-Camp. A community run, 5-day PyTorch Deep Learning Bootcamp
8. AlphaPose. Multi-Person Pose Estimation System

gpu-acceleration

1. tfjs-core. WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
2. tfjs. A WebGL accelerated, browser based JavaScript library for training and deploying ML models.

gradient-boosting

1. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
2. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
3. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
4. auto_ml. Automated machine learning for analytics & production

graph

1. gun. A realtime, decentralized, offline-first, graph database engine.
2. darkflow. Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

h2o

1. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
2. benchm-ml. A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
3. sparkling-water. Sparkling Water provides H2O functionality inside Spark cluster
4. h2o-tutorials. Tutorials and training material for the H2O Machine Learning Platform

hadoop

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)

high-performance-computing

1. julia. The Julia Language: A fresh approach to technical computing.
2. vectorious. High performance linear algebra.

hyperparameter-optimization

1. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
2. xcessiv. A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
3. auto_ml. Automated machine learning for analytics & production

image-classification

1. have-fun-with-machine-learning. An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
2. deepdetect. Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow, XGBoost and TSNE
3. awesome-project-ideas. Curated list of Machine Learning, NLP, Vision Project Ideas

image-processing

1. darkflow. Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
2. framework. Machine learning, computer vision, statistics and general scientific computing for .NET
3. eos. A lightweight 3D Morphable Face Model fitting library in modern C++11/14

image-recognition

1. ssd.pytorch. A PyTorch Implementation of Single Shot MultiBox Detector
2. kur. Descriptive Deep Learning

imagenet

1. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
2. tensorpack. A Neural Net Training Interface on TensorFlow
3. one-pixel-attack-keras. Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on cifar10

information-extraction

1. MITIE. MITIE: library and tools for information extraction
2. snips-nlu. Snips Python library to extract meaning from text
3. snorkel. A system for quickly generating training data with weak supervision

ios

1. Swift-AI. The Swift machine learning library.
2. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
3. Bender. Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
4. Forge. A neural network toolkit for Metal
5. CoreML-in-ARKit. Simple project to detect objects and display 3D labels above them in AR. This serves as a basic template for an ARKit project to use CoreML.
6. MobileNet-CoreML. The MobileNet neural network using Apple's new CoreML framework
7. FaceRecognition-in-ARKit. Detects faces using the Vision-API and runs the extracted face through a CoreML-model to identiy the specific persons.

ios11

1. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
2. ChineseIDCardOCR. [Deprecated] 🇨🇳中国二代身份证光学识别

iot

1. gun. A realtime, decentralized, offline-first, graph database engine.
2. uTensor. AI inference library based on mbed and TensorFlow

ipynb

1. machine_learning_basics. Plain python implementations of basic machine learning algorithms
2. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian

ipython

1. pipeline. PipelineAI: Real-Time Enterprise AI Platform
2. spark-py-notebooks. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

ipython-notebook

1. dive-into-machine-learning. Dive into Machine Learning with Python Jupyter notebook and scikit-learn!
2. Data-Analysis-and-Machine-Learning-Projects. Repository of teaching materials, code, and data for my data analysis and machine learning projects.
3. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
4. spark-py-notebooks. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
5. kaggle-titanic. A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

java

1. CNTK. Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
2. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
3. MITIE. MITIE: library and tools for information extraction
4. oryx. Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning
5. tensorflow_template_application. TensorFlow template application for deep learning
6. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes
7. datumbox-framework. Datumbox is an open-source Machine Learning framework written in Java which allows the rapid development of Machine Learning and Statistical applications.

javascript

1. tfjs-core. WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
2. tfjs. A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
3. neurojs. A javascript deep learning and reinforcement learning library.
4. keras-js. Run Keras models in the browser, with GPU support using WebGL
5. ml. Machine learning tools in JavaScript
6. HackPrincetonF16. Chrome extension to flag fake news on Facebook
7. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
8. lycheejs. 🌱 Next-Gen AI-Assisted Isomorphic Application Engine for Embedded, Console, Mobile, Server and Desktop
9. node-tensorflow. Node.js + TensorFlow
10. vectorious. High performance linear algebra.

jupyter

1. machine-learning-mindmap. A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
2. gophernotes. The Go kernel for Jupyter notebooks and nteract.

jupyter-notebook

1. dive-into-machine-learning. Dive into Machine Learning with Python Jupyter notebook and scikit-learn!
2. handson-ml. A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
3. pipeline. PipelineAI: Real-Time Enterprise AI Platform
4. scikit-learn-videos. Jupyter notebooks from the scikit-learn video series
5. machine-learning. 🌎 machine learning algorithms tutorials (mainly in Python3)
6. gophernotes. The Go kernel for Jupyter notebooks and nteract.
7. DAT8. General Assembly's Data Science course in Washington, DC
8. CADL. Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL
9. lucid. A collection of infrastructure and tools for research in neural network interpretability.

kafka

1. pipeline. PipelineAI: Real-Time Enterprise AI Platform
2. oryx. Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning
3. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes

kaggle

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
3. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
4. Deep-Learning-Boot-Camp. A community run, 5-day PyTorch Deep Learning Bootcamp
5. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
6. MLBox. MLBox is a powerful Automated Machine Learning python library.

kaggle-competition

1. Deep-Learning-Boot-Camp. A community run, 5-day PyTorch Deep Learning Bootcamp
2. kaggle-titanic. A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

keras

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. cheatsheets-ai. Essential Cheat Sheets for deep learning and machine learning researchers
3. keras-js. Run Keras models in the browser, with GPU support using WebGL
4. keras-rl. Deep Reinforcement Learning for Keras.
5. deepjazz. Deep learning driven jazz generation using Keras & Theano!
6. horovod. Distributed training framework for TensorFlow.
7. keras-vis. Neural network visualization toolkit for keras
8. polyaxon. An open source platform for reproducible machine learning at scale
9. turkce-yapay-zeka-kaynaklari. Türkiye'de yapılan derin öğrenme (deep learning) ve makine öğrenmesi (machine learning) çalışmalarının derlendiği sayfa.
10. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
11. one-pixel-attack-keras. Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on cifar10
12. auto_ml. Automated machine learning for analytics & production
13. devol. Automated deep neural network design via genetic algorithms
14. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials
15. Netron. Visualizer for deep learning and machine learning models
16. NeuralKart. A Real-time Mario Kart AI using CNNs, Offline Search, and DAGGER
17. boltzmann-machines. Boltzmann Machines in TensorFlow with examples
18. keras-contrib. Keras community contributions
19. FaceRank. FaceRank - Rank Face by CNN Model based on TensorFlow (add keras version). FaceRank-人脸打分基于 TensorFlow (新增 Keras 版本) 的 CNN 模型（QQ群：522785813）。技术支持：http://tensorflow123.com
20. anago. Bidirectional LSTM-CRF for Sequence Labeling. Easy-to-use and state-of-the-art performance.
21. MLBox. MLBox is a powerful Automated Machine Learning python library.

kubernetes

1. pipeline. PipelineAI: Real-Time Enterprise AI Platform
2. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes
3. polyaxon. An open source platform for reproducible machine learning at scale

language

1. ChatterBot. ChatterBot is a machine learning, conversational dialog engine for creating chat bots
2. awesome-nlp. 📖 A curated list of resources dedicated to Natural Language Processing (NLP)

learning

1. dive-into-machine-learning. Dive into Machine Learning with Python Jupyter notebook and scikit-learn!
2. machine-learning-mindmap. A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
3. mit-deep-learning-book-pdf. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
4. HyperGAN. A composable Generative Adversarial Network(GAN) with API and command line tool.

learning-to-rank

1. lightfm. A Python implementation of LightFM, a hybrid recommendation algorithm.
2. spotlight. Deep recommender models using PyTorch.

lightgbm

1. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
2. DMTK. Microsoft Distributed Machine Learning Toolkit
3. auto_ml. Automated machine learning for analytics & production
4. eli5. A library for debugging/inspecting machine learning classifiers and explaining their predictions
5. MLBox. MLBox is a powerful Automated Machine Learning python library.

linear-algebra

1. mit-deep-learning-book-pdf. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
2. hackermath. Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way
3. gosl. Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more.
4. owl. Owl is an OCaml library for scientific and engineering computing.
5. vectorious. High performance linear algebra.

linear-regression

1. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
2. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
3. machine_learning_basics. Plain python implementations of basic machine learning algorithms
4. DAT8. General Assembly's Data Science course in Washington, DC

linux

1. AlgoWiki. Repository which contains links and resources on different topics of Computer Science.
2. nmap. Nmap - the Network Mapper. Github mirror of official SVN repository.
3. telegram-list. List of telegram groups, channels & bots // Список интересных групп, каналов и ботов телеграма // Список чатов для программистов
4. Dragonfire. Dragonfire is an open-source virtual assistant for Ubuntu based Linux distributions

list

1. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
2. machine-learning-with-ruby. Curated list: Resources for machine learning in Ruby.
3. machine-learning-surveys. A curated list of Machine Learning Surveys, Tutorials and Books.
4. nlp-with-ruby. Practical Natural Language Processing done in Ruby.

logistic-regression

1. python-machine-learning-book. The "Python Machine Learning (1st edition)" book code repository and info resource
2. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
3. machine_learning_basics. Plain python implementations of basic machine learning algorithms
4. DAT8. General Assembly's Data Science course in Washington, DC

lstm

1. tesseract. Tesseract Open Source OCR Engine (main repository)
2. deepjazz. Deep learning driven jazz generation using Keras & Theano!
3. tensorflow_template_application. TensorFlow template application for deep learning
4. LSTM-Human-Activity-Recognition. Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) - Guillaume Chevalier
5. Awesome-Deep-Learning-Resources. Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier

lua

1. nmap. Nmap - the Network Mapper. Github mirror of official SVN repository.
2. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

machine

1. machine-learning-mindmap. A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
2. mit-deep-learning-book-pdf. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

machine-intelligence

1. awesome-artificial-intelligence. A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers
2. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

machinelearning

1. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
2. horovod. Distributed training framework for TensorFlow.
3. Netron. Visualizer for deep learning and machine learning models
4. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

macos

1. Swift-AI. The Swift machine learning library.
2. EmojiIntelligence. Neural Network built in Apple Playground using Swift

matplotlib

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. cheatsheets-ai. Essential Cheat Sheets for deep learning and machine learning researchers
3. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian
4. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
5. yellowbrick. Visual analysis and diagnostic tools to facilitate machine learning model selection.

matrix

1. owl. Owl is an OCaml library for scientific and engineering computing.
2. vectorious. High performance linear algebra.

matrix-factorization

1. lightfm. A Python implementation of LightFM, a hybrid recommendation algorithm.
2. spotlight. Deep recommender models using PyTorch.
3. implicit. Fast Python Collaborative Filtering for Implicit Datasets
4. fastFM. fastFM: A Library for Factorization Machines

mcmc

1. owl. Owl is an OCaml library for scientific and engineering computing.
2. boltzmann-machines. Boltzmann Machines in TensorFlow with examples

metal

1. Bender. Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
2. Forge. A neural network toolkit for Metal

microservices

1. pipeline. PipelineAI: Real-Time Enterprise AI Platform
2. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes

microsoft

1. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
2. DMTK. Microsoft Distributed Machine Learning Toolkit
3. telegram-list. List of telegram groups, channels & bots // Список интересных групп, каналов и ботов телеграма // Список чатов для программистов
4. mmlspark. Microsoft Machine Learning for Apache Spark

ml

1. tensorflow. Computation using data flow graphs for scalable machine learning
2. gun. A realtime, decentralized, offline-first, graph database engine.
3. caffe2. Caffe2 is a lightweight, modular, and scalable deep learning framework.
4. handson-ml. A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
5. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
6. machine-learning-with-ruby. Curated list: Resources for machine learning in Ruby.
7. ml. Machine learning tools in JavaScript
8. jubatus. Framework and Library for Distributed Online Machine Learning
9. mmlspark. Microsoft Machine Learning for Apache Spark
10. Netron. Visualizer for deep learning and machine learning models
11. DLTK. Deep Learning Toolkit for Medical Image Analysis

mnist

1. fashion-mnist. A MNIST-like fashion product database. Benchmark 👉
2. capsule-networks. A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".

model

1. angel. A Flexible and Powerful Parameter Server for large-scale machine learning
2. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)

model-selection

1. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
2. yellowbrick. Visual analysis and diagnostic tools to facilitate machine learning model selection.

mongodb

1. Winds. NOTICE: Winds v2.0 is under active development and will be released in early 2018. It's feature packed with all kinds of goodies and we're excited for you to play with/experience them in the next release. Please stay tuned for updates at https://getstream.io/blog. For a quick read on Winds v2.0, check out the following blog post: https://medium.com/getstream-io/announcing-winds-2-0-an-electron-app-with-support-for-rss-podcasts-d13dbe812477. Thank you for your support! 🚀
2. sacred. Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

mooc

1. CADL. Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL
2. vvedenie-mashinnoe-obuchenie. 📝 Подборка ресурсов по машинному обучению

multi-threading

1. openpose. OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation
2. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)

music

1. deepjazz. Deep learning driven jazz generation using Keras & Theano!
2. DeepAudioClassification. Finding the genre of a song with Deep Learning

mxnet

1. incubator-mxnet. Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
2. polyaxon. An open source platform for reproducible machine learning at scale
3. Netron. Visualizer for deep learning and machine learning models

naive-bayes

1. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
2. DAT8. General Assembly's Data Science course in Washington, DC

named-entity-recognition

1. snips-nlu. Snips Python library to extract meaning from text
2. NeuroNER. Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
3. anago. Bidirectional LSTM-CRF for Sequence Labeling. Easy-to-use and state-of-the-art performance.

natural-language-processing

1. lectures. Oxford Deep NLP 2017 course
2. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
3. gensim. Topic Modelling for Humans
4. nltk. NLTK Source
5. pattern. Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
6. stanford-tensorflow-tutorials. This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
7. awesome-nlp. 📖 A curated list of resources dedicated to Natural Language Processing (NLP)
8. MITIE. MITIE: library and tools for information extraction
9. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
10. machine_learning_examples. A collection of machine learning examples and tutorials.
11. ltp. Language Technology Platform
12. sling. SLING - A natural language frame semantics parser
13. libpostal. A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.
14. arXivTimes. repository to research & share the machine learning articles
15. DAT8. General Assembly's Data Science course in Washington, DC
16. iOS_ML. List of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.
17. turkce-yapay-zeka-kaynaklari. Türkiye'de yapılan derin öğrenme (deep learning) ve makine öğrenmesi (machine learning) çalışmalarının derlendiği sayfa.
18. fastText_multilingual. Multilingual word vectors in 78 languages
19. pytextrank. Python implementation of TextRank for text document NLP parsing and summarization
20. nlp-with-ruby. Practical Natural Language Processing done in Ruby.
21. PyTorch-NLP. Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)
22. sense2vec. 🦆 Use NLP to go beyond vanilla word2vec
23. thinc. 🔮 spaCy's Machine Learning library for NLP in Python
24. jieba-php. "結巴"中文分詞：做最好的 PHP 中文分詞、中文斷詞組件。 / "Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best PHP Chinese word segmentation module.
25. scattertext. Beautiful visualizations of how language differs among document types.
26. ai-deadlines. ⏰ AI conference deadline countdowns
27. awesome-embedding-models. A curated list of awesome embedding models tutorials, projects and communities.
28. DeepMoji. State-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc.
29. anago. Bidirectional LSTM-CRF for Sequence Labeling. Easy-to-use and state-of-the-art performance.

neural-network

1. tensorflow. Computation using data flow graphs for scalable machine learning
2. CNTK. Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
3. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
4. awesome-deep-learning. A curated list of awesome Deep Learning tutorials, projects and communities.
5. python-machine-learning-book. The "Python Machine Learning (1st edition)" book code repository and info resource
6. tflearn. Deep learning library featuring a higher-level API for TensorFlow.
7. tfjs-core. WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
8. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
9. EffectiveTensorflow. TensorFlow tutorials and best practices.
10. handson-ml. A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
11. gensim. Topic Modelling for Humans
12. tfjs. A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
13. php-ml. PHP-ML - Machine Learning library for PHP
14. have-fun-with-machine-learning. An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
15. neurojs. A javascript deep learning and reinforcement learning library.
16. tiny-dnn. header only, dependency-free deep learning framework in C++14
17. TensorFlow-Tutorials. TensorFlow Tutorials with YouTube Videos
18. TensorFlow-World. 🌎 Simple and ready-to-use tutorials for TensorFlow
19. chainer. A flexible framework of neural networks for deep learning
20. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
21. tutorials. 机器学习相关教程
22. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
23. framework. Machine learning, computer vision, statistics and general scientific computing for .NET
24. pipeline. PipelineAI: Real-Time Enterprise AI Platform
25. mit-deep-learning-book-pdf. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
26. machine_learning_basics. Plain python implementations of basic machine learning algorithms
27. deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
28. gorgonia. Gorgonia is a library that helps facilitate machine learning in Go.
29. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
30. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
31. sling. SLING - A natural language frame semantics parser
32. LSTM-Human-Activity-Recognition. Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) - Guillaume Chevalier
33. iOS_ML. List of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.
34. CADL. Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL
35. PyTorch-Tutorial. Build your neural network easy and fast
36. Forge. A neural network toolkit for Metal
37. Deep-Learning-for-Recommendation-Systems. This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
38. ComputeLibrary. The ARM Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies.
39. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
40. one-pixel-attack-keras. Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on cifar10
41. kur. Descriptive Deep Learning
42. devol. Automated deep neural network design via genetic algorithms
43. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials
44. Netron. Visualizer for deep learning and machine learning models
45. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
46. papers-I-read. A-Paper-A-Week
47. PyTorch-NLP. Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)
48. neataptic. 🚀 Blazing fast neuro-evolution & backpropagation for the browser and Node.js
49. DeepLearning.scala. A simple library for creating complex neural networks
50. owl. Owl is an OCaml library for scientific and engineering computing.
51. DLTK. Deep Learning Toolkit for Medical Image Analysis

neural-networks

1. keras. Deep Learning for humans
2. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
3. Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources
4. MLAlgorithms. Minimal and clean examples of machine learning algorithms
5. DeepSpeech. A TensorFlow implementation of Baidu's DeepSpeech architecture
6. keras-js. Run Keras models in the browser, with GPU support using WebGL
7. chainer. A flexible framework of neural networks for deep learning
8. edward. A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
9. DeepLearningProject. An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.
10. mit-deep-learning-book-pdf. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
11. keras-rl. Deep Reinforcement Learning for Keras.
12. machine_learning_basics. Plain python implementations of basic machine learning algorithms
13. tensorpack. A Neural Net Training Interface on TensorFlow
14. deepjazz. Deep learning driven jazz generation using Keras & Theano!
15. Augmentor. Image augmentation library in Python for machine learning.
16. Bender. Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
17. keras-vis. Neural network visualization toolkit for keras
18. AI-Blocks. A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models!
19. NeuroNER. Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
20. 3D-Machine-Learning. A resource repository for 3D machine learning
21. vvedenie-mashinnoe-obuchenie. 📝 Подборка ресурсов по машинному обучению
22. nips2017. A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2017
23. kur. Descriptive Deep Learning
24. DeepAudioClassification. Finding the genre of a song with Deep Learning
25. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials
26. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
27. keras-contrib. Keras community contributions
28. DLTK. Deep Learning Toolkit for Medical Image Analysis
29. tensorflow-value-iteration-networks. TensorFlow implementation of the Value Iteration Networks (NIPS '16) paper
30. sciblog_support. Support content for my blog

neuroevolution

1. FlappyLearning. Program learning to play Flappy Bird by machine learning (Neuroevolution)
2. Machine-Learning-Flappy-Bird. Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

nlp

1. lectures. Oxford Deep NLP 2017 course
2. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
3. gensim. Topic Modelling for Humans
4. nltk. NLTK Source
5. stanford-tensorflow-tutorials. This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
6. awesome-nlp. 📖 A curated list of resources dedicated to Natural Language Processing (NLP)
7. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
8. rasa_nlu. turn natural language into structured data
9. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
10. snips-nlu. Snips Python library to extract meaning from text
11. ltp. Language Technology Platform
12. sling. SLING - A natural language frame semantics parser
13. libpostal. A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.
14. papers. Summaries of machine learning papers
15. datumbox-framework. Datumbox is an open-source Machine Learning framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
16. NeuroNER. Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
17. vvedenie-mashinnoe-obuchenie. 📝 Подборка ресурсов по машинному обучению
18. fastText_multilingual. Multilingual word vectors in 78 languages
19. pytextrank. Python implementation of TextRank for text document NLP parsing and summarization
20. nlp-with-ruby. Practical Natural Language Processing done in Ruby.
21. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials
22. rasa_core. machine learning based dialogue engine for conversational software
23. eli5. A library for debugging/inspecting machine learning classifiers and explaining their predictions
24. PyTorch-NLP. Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)
25. sense2vec. 🦆 Use NLP to go beyond vanilla word2vec
26. thinc. 🔮 spaCy's Machine Learning library for NLP in Python
27. jieba-php. "結巴"中文分詞：做最好的 PHP 中文分詞、中文斷詞組件。 / "Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best PHP Chinese word segmentation module.
28. scattertext. Beautiful visualizations of how language differs among document types.
29. Dragonfire. Dragonfire is an open-source virtual assistant for Ubuntu based Linux distributions

nlp-library

1. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
2. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

nlu

1. rasa_nlu. turn natural language into structured data
2. snips-nlu. Snips Python library to extract meaning from text

notebook

1. spark-py-notebooks. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
2. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials

numpy

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. cheatsheets-ai. Essential Cheat Sheets for deep learning and machine learning researchers
3. chainer. A flexible framework of neural networks for deep learning
4. tutorials. 机器学习相关教程
5. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian
6. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si

object-detection

1. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
2. darkflow. Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
3. deepdetect. Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow, XGBoost and TSNE
4. luminoth. Deep Learning toolkit for Computer Vision
5. ssd.pytorch. A PyTorch Implementation of Single Shot MultiBox Detector

ocr

1. tesseract. Tesseract Open Source OCR Engine (main repository)
2. Swift-AI. The Swift machine learning library.
3. crnn. Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition.

opencv

1. openpose. OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation
2. BossSensor. Hide screen when boss is approaching.
3. opencv. OpenCV projects: Face Recognition, Machine Learning, Colormaps, Local Binary Patterns, Examples...

opensource

1. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
2. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R

optimization

1. gosl. Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more.
2. scikit-optimize. Sequential model-based optimization with a scipy.optimize interface
3. owl. Owl is an OCaml library for scientific and engineering computing.
4. MLBox. MLBox is a powerful Automated Machine Learning python library.

pandas

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian
3. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
4. DAT8. General Assembly's Data Science course in Washington, DC
5. weld. High-performance runtime for data analytics applications
6. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

papers

1. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
2. 3D-Machine-Learning. A resource repository for 3D machine learning
3. awesome-embedding-models. A curated list of awesome embedding models tutorials, projects and communities.

parallel

1. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
2. ray. A high-performance distributed execution engine
3. pomegranate. Fast, flexible and easy to use probabilistic modelling in Python.
4. root. The official ROOT repository

pipeline

1. pipeline. PipelineAI: Real-Time Enterprise AI Platform
2. MLBox. MLBox is a powerful Automated Machine Learning python library.

plotting

1. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
2. scikit-plot. An intuitive library to add plotting functionality to scikit-learn objects.
3. gosl. Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more.
4. owl. Owl is an OCaml library for scientific and engineering computing.

policy-gradient

1. reinforcement-learning. Minimal and Clean Reinforcement Learning Examples
2. Reinforcement-learning-with-tensorflow. Simple Reinforcement learning tutorials

prediction

1. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes
2. bulbea. 🐗 🐻 Deep Learning based Python Library for Stock Market Prediction and Modelling
3. MLBox. MLBox is a powerful Automated Machine Learning python library.

predictive-modeling

1. ISLR-python. An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
2. mlr. mlr: Machine Learning in R

probabilistic-programming

1. edward. A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
2. pyro. Deep universal probabilistic programming with Python and PyTorch
3. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
4. pystan. PyStan, the Python interface to Stan

procedural-generation

1. WaveFunctionCollapse. Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics.
2. SynTex. Texture synthesis from examples.

programming

1. telegram-list. List of telegram groups, channels & bots // Список интересных групп, каналов и ботов телеграма // Список чатов для программистов
2. handong1587.github.io.

programming-language

1. cs-video-courses. List of Computer Science courses with video lectures.
2. julia. The Julia Language: A fresh approach to technical computing.

pyspark

1. spark-py-notebooks. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
2. mmlspark. Microsoft Machine Learning for Apache Spark

python-library

1. turicreate. Turi Create simplifies the development of custom machine learning models.
2. bulbea. 🐗 🐻 Deep Learning based Python Library for Stock Market Prediction and Modelling

pytorch

1. fastai. The fast.ai deep learning library, lessons, and tutorials
2. pyro. Deep universal probabilistic programming with Python and PyTorch
3. generative-models. Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
4. deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
5. tensorboard-pytorch. tensorboard for pytorch (and chainer, mxnet, numpy, ...)
6. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
7. skorch. A scikit-learn compatible neural network library that wraps pytorch
8. PyTorch-Tutorial. Build your neural network easy and fast
9. polyaxon. An open source platform for reproducible machine learning at scale
10. spotlight. Deep recommender models using PyTorch.
11. Deep-Learning-Boot-Camp. A community run, 5-day PyTorch Deep Learning Bootcamp
12. turkce-yapay-zeka-kaynaklari. Türkiye'de yapılan derin öğrenme (deep learning) ve makine öğrenmesi (machine learning) çalışmalarının derlendiği sayfa.
13. capsule-networks. A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".
14. AlphaPose. Multi-Person Pose Estimation System
15. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
16. ssd.pytorch. A PyTorch Implementation of Single Shot MultiBox Detector
17. pytorch-generative-adversarial-networks. A very simple generative adversarial network (GAN) in PyTorch
18. PyTorch-NLP. Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)

pytorch-tutorial

1. PyTorch-Tutorial. Build your neural network easy and fast
2. Deep-Learning-Boot-Camp. A community run, 5-day PyTorch Deep Learning Bootcamp

pytorch-tutorials

1. Awesome-pytorch-list. A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
2. Deep-Learning-Boot-Camp. A community run, 5-day PyTorch Deep Learning Bootcamp

quant

1. abu. 阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构
2. awesome-quant. 中国的Quant相关资源索引

quantitative-trading

1. abu. 阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构
2. bulbea. 🐗 🐻 Deep Learning based Python Library for Stock Market Prediction and Modelling

r

1. LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
2. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
3. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
4. benchm-ml. A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
5. mlr. mlr: Machine Learning in R
6. awesome-quant. 中国的Quant相关资源索引
7. h2o-tutorials. Tutorials and training material for the H2O Machine Learning Platform
8. MLPB. Machine Learning Problem Bible | Problem Set Here >>
9. sparklyr. R interface for Apache Spark

r-package

1. mlr. mlr: Machine Learning in R
2. sparklyr. R interface for Apache Spark

random-forest

1. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
2. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
3. benchm-ml. A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
4. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
5. gcForest. This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

rasa

1. rasa_nlu. turn natural language into structured data
2. rasa_core. machine learning based dialogue engine for conversational software

rbm

1. generative-models. Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
2. boltzmann-machines. Boltzmann Machines in TensorFlow with examples

react

1. Winds. NOTICE: Winds v2.0 is under active development and will be released in early 2018. It's feature packed with all kinds of goodies and we're excited for you to play with/experience them in the next release. Please stay tuned for updates at https://getstream.io/blog. For a quick read on Winds v2.0, check out the following blog post: https://medium.com/getstream-io/announcing-winds-2-0-an-electron-app-with-support-for-rss-podcasts-d13dbe812477. Thank you for your support! 🚀
2. klassify. Bayesian Text classification service based on Redis. Built on top of Tornado and React.js

real-time

1. openpose. OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation
2. darkflow. Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

recommender-system

1. lightfm. A Python implementation of LightFM, a hybrid recommendation algorithm.
2. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes
3. spotlight. Deep recommender models using PyTorch.
4. Deep-Learning-for-Recommendation-Systems. This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
5. implicit. Fast Python Collaborative Filtering for Implicit Datasets
6. fastFM. fastFM: A Library for Factorization Machines

recurrent-neural-networks

1. deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
2. LSTM-Human-Activity-Recognition. Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) - Guillaume Chevalier
3. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials

regression

1. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
2. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
3. mlpack. mlpack: a scalable C++ machine learning library --
4. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
5. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
6. PyTorch-Tutorial. Build your neural network easy and fast
7. mlr. mlr: Machine Learning in R
8. owl. Owl is an OCaml library for scientific and engineering computing.
9. MLBox. MLBox is a powerful Automated Machine Learning python library.

reinforcement-learning

1. pysc2. StarCraft II Learning Environment
2. neurojs. A javascript deep learning and reinforcement learning library.
3. TensorFlow-Tutorials. TensorFlow Tutorials with YouTube Videos
4. TensorFlow-Book. Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
5. awesome-artificial-intelligence. A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers
6. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
7. ray. A high-performance distributed execution engine
8. keras-rl. Deep Reinforcement Learning for Keras.
9. tensorpack. A Neural Net Training Interface on TensorFlow
10. reinforcement-learning. Minimal and Clean Reinforcement Learning Examples
11. machine_learning_examples. A collection of machine learning examples and tutorials.
12. Reinforcement-learning-with-tensorflow. Simple Reinforcement learning tutorials
13. arXivTimes. repository to research & share the machine learning articles
14. TorchCraft. Connecting Torch to StarCraft
15. PyTorch-Tutorial. Build your neural network easy and fast
16. polyaxon. An open source platform for reproducible machine learning at scale
17. FlappyBirdRL. Flappy Bird hack using Reinforcement Learning
18. DoYouEvenLearn. Essential Guide to keep up with AI/ML/CV/UNameIt
19. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials
20. tensorflow-value-iteration-networks. TensorFlow implementation of the Value Iteration Networks (NIPS '16) paper

reproducibility

1. sacred. Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
2. dvc. Git for data scientists - manage your code and data together

restricted-boltzmann-machine

1. generative-models. Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
2. boltzmann-machines. Boltzmann Machines in TensorFlow with examples

rnn

1. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
2. deepjazz. Deep learning driven jazz generation using Keras & Theano!
3. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
4. LSTM-Human-Activity-Recognition. Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) - Guillaume Chevalier
5. PyTorch-Tutorial. Build your neural network easy and fast

robotics

1. cs-video-courses. List of Computer Science courses with video lectures.
2. ai-deadlines. ⏰ AI conference deadline countdowns

ruby

1. machine-learning-with-ruby. Curated list: Resources for machine learning in Ruby.
2. decisiontree. ID3-based implementation of the ML Decision Tree algorithm
3. nlp-with-ruby. Practical Natural Language Processing done in Ruby.

rubyml

1. machine-learning-with-ruby. Curated list: Resources for machine learning in Ruby.
2. decisiontree. ID3-based implementation of the ML Decision Tree algorithm
3. nlp-with-ruby. Practical Natural Language Processing done in Ruby.

rubynlp

1. machine-learning-with-ruby. Curated list: Resources for machine learning in Ruby.
2. nlp-with-ruby. Practical Natural Language Processing done in Ruby.

rust

1. rust. Rust language bindings for TensorFlow
2. weld. High-performance runtime for data analytics applications
3. rusty-machine. Machine Learning library for Rust

scala

1. angel. A Flexible and Powerful Parameter Server for large-scale machine learning
2. tensorflow_template_application. TensorFlow template application for deep learning
3. mmlspark. Microsoft Machine Learning for Apache Spark
4. DeepLearning.scala. A simple library for creating complex neural networks

science

1. awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems.
2. machine-learning-mindmap. A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
3. awesome-awesome. A curated list of awesome curated lists of many topics.
4. deepvariant. DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

scientific-computing

1. julia. The Julia Language: A fresh approach to technical computing.
2. gosl. Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more.
3. scikit-optimize. Sequential model-based optimization with a scipy.optimize interface
4. owl. Owl is an OCaml library for scientific and engineering computing.

scikit-learn

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. python-machine-learning-book. The "Python Machine Learning (1st edition)" book code repository and info resource
3. dive-into-machine-learning. Dive into Machine Learning with Python Jupyter notebook and scikit-learn!
4. handson-ml. A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
5. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
6. scikit-learn-videos. Jupyter notebooks from the scikit-learn video series
7. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian
8. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
9. DAT8. General Assembly's Data Science course in Washington, DC
10. featuretools. automated feature engineering
11. scikit-plot. An intuitive library to add plotting functionality to scikit-learn objects.
12. skorch. A scikit-learn compatible neural network library that wraps pytorch
13. python-machine-learning-book-2nd-edition. The "Python Machine Learning (2nd edition)" book code repository and info resource
14. xcessiv. A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
15. nolearn. scikit-learn compatible neural network library
16. sparkit-learn. PySpark + Scikit-learn = Sparkit-learn
17. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
18. auto_ml. Automated machine learning for analytics & production
19. eli5. A library for debugging/inspecting machine learning classifiers and explaining their predictions
20. yellowbrick. Visual analysis and diagnostic tools to facilitate machine learning model selection.
21. gplearn. Genetic Programming in Python, with a scikit-learn inspired API

scipy

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. cheatsheets-ai. Essential Cheat Sheets for deep learning and machine learning researchers
3. mlcourse_open. OpenDataScience Machine Learning course. Both in English and Russian
4. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si

security

1. cs-video-courses. List of Computer Science courses with video lectures.
2. nmap. Nmap - the Network Mapper. Github mirror of official SVN repository.
3. cleverhans. An adversarial example library for constructing attacks, building defenses, and benchmarking both

self-driving-car

1. neurojs. A javascript deep learning and reinforcement learning library.
2. vehicle-detection. Vehicle detection using machine learning and computer vision techniques for Udacity's self-driving car course.

sentiment-analysis

1. pattern. Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
2. nlp-with-ruby. Practical Natural Language Processing done in Ruby.
3. bulbea. 🐗 🐻 Deep Learning based Python Library for Stock Market Prediction and Modelling
4. scattertext. Beautiful visualizations of how language differs among document types.

spacy

1. spaCy. 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
2. rasa_nlu. turn natural language into structured data
3. sense2vec. 🦆 Use NLP to go beyond vanilla word2vec
4. thinc. 🔮 spaCy's Machine Learning library for NLP in Python
5. Dragonfire. Dragonfire is an open-source virtual assistant for Ubuntu based Linux distributions
6. neuralcoref. ✨State-of-the-art coreference resolution based on neural nets and spaCy

spark

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. angel. A Flexible and Powerful Parameter Server for large-scale machine learning
3. h2o-3. Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
4. pipeline. PipelineAI: Real-Time Enterprise AI Platform
5. TensorFlowOnSpark. TensorFlowOnSpark brings TensorFlow programs onto Apache Spark clusters
6. benchm-ml. A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
7. tensorflow_template_application. TensorFlow template application for deep learning
8. seldon-server. Machine Learning Platform and Recommendation Engine built on Kubernetes
9. spark-ml-source-analysis. spark ml 算法原理剖析以及具体的源码实现分析
10. spark-py-notebooks. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
11. mmlspark. Microsoft Machine Learning for Apache Spark
12. sparkling-water. Sparkling Water provides H2O functionality inside Spark cluster

speech-recognition

1. DeepSpeech. A TensorFlow implementation of Baidu's DeepSpeech architecture
2. iOS_ML. List of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.
3. kur. Descriptive Deep Learning
4. Dragonfire. Dragonfire is an open-source virtual assistant for Ubuntu based Linux distributions

speech-to-text

1. DeepSpeech. A TensorFlow implementation of Baidu's DeepSpeech architecture
2. kur. Descriptive Deep Learning
3. Dragonfire. Dragonfire is an open-source virtual assistant for Ubuntu based Linux distributions

stacking

1. mlr. mlr: Machine Learning in R
2. MLBox. MLBox is a powerful Automated Machine Learning python library.

stanford

1. stanford-tensorflow-tutorials. This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
2. weld. High-performance runtime for data analytics applications

statistics

1. scikit-learn. scikit-learn: machine learning in Python
2. edward. A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
3. framework. Machine learning, computer vision, statistics and general scientific computing for .NET
4. imbalanced-learn. Python module to perform under sampling and over sampling with various techniques.
5. xlearn. High Performance, Easy-to-use, and Scalable Machine Learning Package (C++, Python, R)
6. hackermath. Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way
7. datumbox-framework. Datumbox is an open-source Machine Learning framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
8. mlr. mlr: Machine Learning in R
9. ml-videos. A collection of video resources for machine learning
10. awesome-quant. 中国的Quant相关资源索引
11. root. The official ROOT repository
12. owl. Owl is an OCaml library for scientific and engineering computing.
13. pystan. PyStan, the Python interface to Stan

stock-market

1. Clairvoyant. Software designed to identify and monitor social/historical cues for short term stock movement
2. bulbea. 🐗 🐻 Deep Learning based Python Library for Stock Market Prediction and Modelling

supervised-learning

1. mlxtend. A library of extension and helper modules for Python's data analysis and machine learning libraries.
2. HyperGAN. A composable Generative Adversarial Network(GAN) with API and command line tool.

support-vector-machines

1. framework. Machine learning, computer vision, statistics and general scientific computing for .NET
2. Clairvoyant. Software designed to identify and monitor social/historical cues for short term stock movement

svm

1. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
2. vehicle-detection. Vehicle detection using machine learning and computer vision techniques for Udacity's self-driving car course.

swift

1. Swift-AI. The Swift machine learning library.
2. Bender. Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
3. EmojiIntelligence. Neural Network built in Apple Playground using Swift
4. iOS_ML. List of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.
5. Forge. A neural network toolkit for Metal
6. ChineseIDCardOCR. [Deprecated] 🇨🇳中国二代身份证光学识别
7. MobileNet-CoreML. The MobileNet neural network using Apple's new CoreML framework

tensorboard

1. tensorflow_cookbook. Code for Tensorflow Machine Learning Cookbook
2. tensorboard-pytorch. tensorboard for pytorch (and chainer, mxnet, numpy, ...)
3. tensorflow_template_application. TensorFlow template application for deep learning

tensorflow-models

1. Awesome-CoreML-Models. Largest list of models for Core ML (for iOS 11+)
2. AndroidTensorFlowMachineLearningExample. Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
3. Netron. Visualizer for deep learning and machine learning models
4. boltzmann-machines. Boltzmann Machines in TensorFlow with examples

tensorflow-tutorials

1. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
2. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
3. Reinforcement-learning-with-tensorflow. Simple Reinforcement learning tutorials
4. AndroidTensorFlowMachineLearningExample. Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
5. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
6. Awesome-TensorFlow-Chinese. Awesome-TensorFlow-Chinese，TensorFlow 中文资源精选，官方网站，安装教程，入门教程，视频教程，实战项目，学习路径。QQ群：522785813，微信群二维码：http://www.tensorflownews.com/
7. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials

tensorlayer

1. tensorlayer. TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
2. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials

text-classification

1. snips-nlu. Snips Python library to extract meaning from text
2. fastText.py. A Python interface for Facebook fastText

text-mining

1. awesome-nlp. 📖 A curated list of resources dedicated to Natural Language Processing (NLP)
2. scattertext. Beautiful visualizations of how language differs among document types.

tflearn

1. tflearn. Deep learning library featuring a higher-level API for TensorFlow.
2. tensorflow-tutorial. TensorFlow and Deep Learning Tutorials

theano

1. data-science-ipython-notebooks. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
2. tutorials. 机器学习相关教程
3. keras-rl. Deep Reinforcement Learning for Keras.
4. deepjazz. Deep learning driven jazz generation using Keras & Theano!
5. keras-vis. Neural network visualization toolkit for keras
6. keras-contrib. Keras community contributions

topic-modeling

1. gensim. Topic Modelling for Humans
2. owl. Owl is an OCaml library for scientific and engineering computing.

torch

1. DIGITS. Deep Learning GPU Training System
2. TorchCraft. Connecting Torch to StarCraft
3. AlphaPose. Multi-Person Pose Estimation System
4. Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
5. neuralconvo. Neural conversational model in Torch

tutorial

1. TensorFlow-Examples. TensorFlow Tutorial and Examples for Beginners with Latest APIs
2. stanford-tensorflow-tutorials. This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
3. have-fun-with-machine-learning. An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
4. TensorFlow-Tutorials. TensorFlow Tutorials with YouTube Videos
5. DeepLearningProject. An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.
6. catboost. CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
7. scikit-learn-videos. Jupyter notebooks from the scikit-learn video series
8. Tensorflow-Tutorial. Tensorflow tutorial from basic to hard
9. Reinforcement-learning-with-tensorflow. Simple Reinforcement learning tutorials
10. CADL. Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL
11. PyTorch-Tutorial. Build your neural network easy and fast
12. mlr. mlr: Machine Learning in R
13. h2o-tutorials. Tutorials and training material for the H2O Machine Learning Platform

typescript

1. tfjs-core. WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
2. tfjs. A WebGL accelerated, browser based JavaScript library for training and deploying ML models.

unsupervised-learning

1. mlxtend. A library of extension and helper modules for Python's data analysis and machine learning libraries.
2. HyperGAN. A composable Generative Adversarial Network(GAN) with API and command line tool.

variational-inference

1. pyro. Deep universal probabilistic programming with Python and PyTorch
2. boltzmann-machines. Boltzmann Machines in TensorFlow with examples

visualization

1. tensorboard-pytorch. tensorboard for pytorch (and chainer, mxnet, numpy, ...)
2. mapd-core. The MapD Core database
3. keras-vis. Neural network visualization toolkit for keras
4. orange3. 🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si
5. scikit-plot. An intuitive library to add plotting functionality to scikit-learn objects.
6. gosl. Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more.
7. lucid. A collection of infrastructure and tools for research in neural network interpretability.
8. yellowbrick. Visual analysis and diagnostic tools to facilitate machine learning model selection.
9. root. The official ROOT repository
10. scattertext. Beautiful visualizations of how language differs among document types.

visualizer

1. Netron. Visualizer for deep learning and machine learning models
2. yellowbrick. Visual analysis and diagnostic tools to facilitate machine learning model selection.

webgl

1. tfjs-core. WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
2. tfjs. A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
3. keras-js. Run Keras models in the browser, with GPU support using WebGL

word-embeddings

1. gensim. Topic Modelling for Humans
2. scattertext. Beautiful visualizations of how language differs among document types.

word-vectors

1. fastText_multilingual. Multilingual word vectors in 78 languages
2. PyTorch-NLP. Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)

word2vec

1. gensim. Topic Modelling for Humans
2. sense2vec. 🦆 Use NLP to go beyond vanilla word2vec
3. scattertext. Beautiful visualizations of how language differs among document types.
4. awesome-embedding-models. A curated list of awesome embedding models tutorials, projects and communities.

xgboost

1. tpot. A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
2. deepdetect. Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow, XGBoost and TSNE
3. benchm-ml. A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
4. auto_ml. Automated machine learning for analytics & production
5. eli5. A library for debugging/inspecting machine learning classifiers and explaining their predictions
6. MLBox. MLBox is a powerful Automated Machine Learning python library.