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

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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

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A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

Teaching Materials for Dr. Waleed A. Yousef

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Quantitative Interview Preparation Guide, updated version here ==>

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Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes

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

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

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

Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

All Stanford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine Learning, Probabilities, Statistics, Algebra and Calculus.