Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

Data Science Roadmap from A to Z

Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra

📙 The probability and statistics cookbook

The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.

The basic distribution probability Tutorial for Deep Learning Researchers

Teaching Materials for Dr. Waleed A. Yousef

Self-study on Larry Wasserman's "All of Statistics"

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

Algorithm is a library of tools that is used to create intelligent applications.

Quantitative Interview Preparation Guide, updated version here ==>

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

数学知识点滴积累 矩阵 数值优化 神经网络反向传播 图优化 概率论 随机过程 卡尔曼滤波 粒子滤波 数学函数拟合

VIP cheatsheets for Stanford's CME 106 Probability and Statistics for Engineers

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI

Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows

Courses, Articles and many more which can help beginners or professionals.

A C++ header-only library of statistical distribution functions.