The leading textbook in Artificial Intelligence. Used in over 1400 universities in over 125 countries. The 22nd most cited computer science publication on Citeseer (and 4th most cited publicati...
By Ian GoodFellow, Yoshua Bengio and Aaron Courville A comprehensive book on the Deep Learning field: a subset of Lachine Learning in AI that has networks capable of learning from unstructured d...
Natural Language processing, for those less familiar with it, involves applying computational techniques to the analysis and synthesis of natural language and speech. If you’re interested in Nat...
Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questio...
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it...
From this page you can download a draft of notes I used for a Stanford course on Machine Learning. Although I have tried to eliminate errors, some undoubtedly remain---caveat lector. Certain elemen...
This book takes a Bayesian statistics approach to Machine Learning. A worthwhile read if you’re looking into getting into the Machine Learning field, though also an important branch for any aspirin...
This text provides a solid introduction to both the computational and theoretical aspects of linear algebra.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, effic...
AI Superpowers: China, Silicon Valley, and the New World Order is a 2018 non-fiction book by Beijing, China-based Kai-Fu Lee, an Artificial Intelligence (AI) pioneer, expert on China and a venture ...
Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the s...
This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recog...
Understanding the differences between the two main types of machine learning methods
Translation of VIP cheatsheets for Machine Learning and Deep Learning
Self-Supervised Learning is getting attention because it has the potential to solve a significant limitation of supervised machine learning, viz. requiring lots of external training samples or supe...