README.md

AI Resources

In this repository you will find books and documents related to Machine Learning and Deep Learning. I try to make the documents as clear as possible, and the goal is being to used as a learning resource or study and a way to lookup problems to solve specific problems. Books and documents I have collected throughout my studies. I hope everyone will like it If you find it useful, please give me a star. It will be the motivation for me to continue developing this repo. If you don't know where to start learning. You can check it out: AI Roadmap. This road will can helps you learn all about ML and DL. You can then go deeper into a specific area

Table Of Contents

• 1 Books
  • 1.1 Database
  • 1.2 Deep Learning
  • 1.3 Machine Learning
  • 1.4 Linear Algebra
• 2 Documents
  • 2.1 Computer Vision
  • 2.2 GAN Models
  • 2.3 Sequence Models
  • 2.4 Metrics
  • 2.5 Optimizer
  • 2.6 Technical

1. Books

1.1 Database

• Database Concept
• Database for Beginners Guide
• Introduction Database

1.2 Deep Learning

• DL with pytorch
• DL basic (VN language)
• Neural Network
• DL Basic (EN language)
• NLP with Python
• Computer Vision Basic

1.3 Machine Learning

• Ml Concept
• ML with Python
• ML with C++

1.4 Linear Algebra

• From University of Vermont
• From UCDAVIS
• From University of Puget Sound

2. Documents

2.1 Computers Vision

• Classification models
  • Mobile Net
  • SE Net
  • Shuffle Net
  • Dense Net
  • Efficient Net
  • Inception Net
  • Overview some models
  • Res Net
  • Squeeze Net
• Image Caption
  • CNN + LSTM
• CNN
  • Automatic Target Recognition
  • CNN from AI VN
  • CNN to ConvNext
  • Condition CNN
  • Segment with Condition CNN
  • Pretrained Models (VN language)
• OCR
  • Text Recognition
  • TrOCR model
• Vision Transformer
  • ViT from PDX University
  • ViT paper
  • ViT or CNN
  • Recent Advances in ViT

2.2 GAN Models

• SRGAN
• DCGAN
• Stack GAN(v2)
• Conditional GAN
• AC-GAN
• InFoGAN
• Stack GAN(v1)
• CycleGAN
• WGAN
• ProGAN

2.3 Sequence Models

• Gate Recurrent Units
• Recurrent NN
• Long Short Term Memory (LSTM)
• Transformer
• Word Embedding
• Manba

2.4 Metrics

• Classification Model Evaluation Metrics
• Evolution Metrics
• ROC Curve
• Visualizer multiple evolution metrics

2.5 Optimizer

• Overview Optimizer
• Adam Optimizer

2.6 Technical

• Batch Normalization
• Layer Normalization
• Transfer Learning
• Dropout Layer
• Graph Mode