Developer-Y/free-technical-ebooks

Collection of free online ebooks

Introduction

• Please add pull request only for books that are made freely available by their authors or publishers. If author or publisher has not provided consent, then those books will not be added to this list.
• Please prioritize quality over quantity while creating PR.

Table of Contents

• Computer Science
• Probability and Statistics
• Machine Learning
• Optimization
• Math
• Physics

Books

• Frank Nielsen - Curated list of many great CS/IT/ML/AI books

Computer Science

• Algorithms by Jeff Erickson
• Computer Vision: Algorithms and Applications, 2nd ed. - Richard Szeliski (1st Edition)
• Quantum Computing for the Quantum Curious
• Site Reliability Engineering - Google
• The Little Book of Semaphores - Allen B. Downey
• Software-Defined Networks: A Systems Approach
• Computer Networks: A Systems Approach
• Edge Cloud Operations: A Systems Approach
• Private 5G: A Systems Approach
• TCP Congestion Control: A Systems Approach
• Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms - Nikolaus Correll, Bradley Hayes, Christoffer Heckman and Alessandro Roncone
• Introduction to Quantum Information Science by Artur Ekert, Timothy Hosgood, Alastair Kay, Chiara Macchiavello (PDF)
• Crafting Interpreters
• Beej's Guide to Network Programming
• Beej's Guide to C Programming
• Programming Languages: Application and Interpretation - Shriram Krishnamurthi
• Introduction to Information Retrieval
• Information Theory - From Coding to Learning

Probability and Statistics

• Algorithmic High-Dimensional Robust Statistics
• Bayesian Data Analysis, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
• Bayesian Modeling and Computation in Python
• Causal Inference - The Mixtape
• Causal Inference: What If - Miguel A. Hern´an, James M. Robins
• Elements of Causal Inference - Peters
• Forecasting: Principles and Practice (3rd ed) by Rob J Hyndman and George Athanasopoulos
• High-Dimensional Probability
• Information Theory: From Coding to Learning by Y. Polyanskiy and Y. Wu
• Introduction to Causal Inference from a Machine Learning Perspective - Brady Neal
• Introduction to Proability 2nd edition - Blitzstein and Hwang
• OpenIntro Statistics
• Prob 140 Textbook - UC Berkeley
• Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
• Statistical Thinking for the 21st Century - Russell A. Poldrack

Machine Learning

• ALGORITHMS FOR DECISION MAKING
• Bayesian Reasoning and Machine Learning
• Bayesian models of perception and action
• Elements of Statistical Learning
• Computer Age Statistical Inference
• Computational and Inferential Thinking - Ani Adhikari and John DeNero
• Computational Topology for Data Analysis
• Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Dive into Deep Learning
• Eisenstein NLP Notes - Gatech
• High-Dimensional Data Analysis with Low-Dimensional Models, John Wright and Yi Ma
• Information Theory, Inference, and Learning Algorithms
• Introduction to Statistical Learning
• Learning Theory from First Principles by Francis Bach
• Natural Language Processing with Python
• Pattern Recognition and Machine Learning - Christopher Bishop
• Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
• Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy
• Python Data Science Handbook by Jake VanderPlas
• Reinforcement Learning: An Introduction
• Mathematical Foundations of Reinforcement Learning
• Algorithms for Reinforcement Learning - Csaba Szepesvari
• Control Systems and Reinforcement Learning - Sean Meyn - UFL
• Understanding Deep Learning
• Computer Vision: Models, Learning, and Inference
• Interpretable Machine Learning
• Machine Learning in Production: From Models to Products - Christian Kästner, CMU
• Deep Learning for Coders with Fastai & PyTorch
• Physics-based Deep Learning
• Foundations of Data Science
• Understanding Machine Learning: From Theory to Algorithms
• Multimodal Deep Learning
• Deep Reinforcement Learning - Aske Plaat
• Speech and Language Processing - Dan Jurafsky and James H. Martin
• Algebra, Topology, Differential Calculus, and Optimization Theory
• Principles and Practice of Deep Representation Learning

Optimization

• ALGORITHMS FOR OPTIMIZATION - MYKEL J. KOCHENDERFER AND TIM A. WHEELER
• An introduction to Optimization on smooth manifolds (with book) - EPFL
• Convex Optimization - Boyd
• Engineering Design Optimization by Joaquim Martins and Andrew Ning
• Optimization for Modern Data Analysis - Benjamin Recht and Stephen J. Wright

Math

• Mathematics for Machine Learning
• Linear Algebra - Jim Hefferon
• Introduction to Probability for Data Science - Stanley H. Chan
• An introduction to Optimization on smooth manifolds
• Mathematical Methods in Data Science by Sebastian Roch

Physics

• Statistical Machine Learning for Astronomy -- A Textbook - Yuan-Sen Ting
• Machine Learning, Statistics, and Data Mining for Heliophysics - Monica Bobra and James Mason
• Feynman Lectures on Physics - Caltech
• An Introduction to Modern Computational Physics
• Data Analysis for Physics