Bookshelf

I am addicted to science and technology books. Fortunately, there are many authors that kindly have made their works available online for free.

Physics

Feynman et al. - The Feynman Lectures on Physics

Biology

Ingalls - Mathematical Modeling in Systems Biology: An Introduction
Batra et al. - The Epidemiologist R Handbook
Milo et al. - Cell Biology by the Numbers
Bonita et al. - Basic Epidemiology, 2nd Ed.
Frank - Immunology and Evolution of Infectious Disease
Frank - Dynamics of Cancer
Rolls - Emotions and Decision-Making Explained
Rolls - Cerebral Cortex: Principles of Operation
Rolls et al. - The Noisy Brain - Stochastic Dynamics as a Principle of Brain Function
Rolls - Neuroculture - On the Implications of Brain Science
Rolls - Memory, Attention, and Decision-Making: a unifying computational neuroscience approach
Rolls et al. - The Computational Neuroscience of Vision

Computer Science

Algorithms and Data Structures

Arora et al. - Computational Complexity: A Modern Approach
Bovet et al. - Introduction to the theory of complexity
Erickson - Algorithms
Di Pierro - Annotated Algorithms in Python
Flajolet et al. - Analytic Combinatorics
Barack - Introduction to Theoretical Computer Science
Wigderson - Mathematics and Computation
Schloss et al. - Algorithm Archive
Kochenderfer et al. - Algorithms for Decision Making
Kochenderfer et al. - Algorithms for Optimization
Needham et al. - Graph Algorithms

Distributed Systems and High Performance Computing

van Steen et al. - Computer and Network Organization
van Steen et al. - Distributed Systems, 3rd Ed.
Burns - Designing Distributed Systems
Eijkhout - Introduction to High Performance Scientific Computing
Livermore Computing Center - Introduction to Parallel Computing Tutorial
Severance - High Performance Computing

Computer Programming

Stepanov et al. - Elements of Programming
Papert - Mindstorm - Children, Computers, and Powerful Ideas

R

Wickham - Advanced R, 2nd Ed.
Phillips - YaRr! The Pirate’s Guide to R
Grolemund - Hand-on Programming with R

JavaScript

Haverbeke - Eloquent JavaScript, 3rd Ed.

Haskell

O’Sullivan et al. - Real World Haskell

Scala

Alexander - Scala Book

Java

Eck - Introduction to Programming Using Java, 8th Ed.

Data Science

Pradhan et al. - R for Data Science
VanderPlas - Python Data Science Handbook
Perlin - Analyzing Financial and Economic Data with R
Adhikari et al. - Computational and Inferential Thinking: The Foundations of Data Science, 2nd Ed.
Zacharski - A Programmer’s Guide to Data Mining
Jeroen - Data Science at the Command Line, 2nd Ed.
Irizarry - Introduction to Data Science
Blum et al. - Foundation of Data Science
Ranjan Das - Data Science: Theories, Models, Algorithms and Analytics
Bouveyron et al. - Model-Based Clustering and Classification for Data Science
Rajamohan - Computational Statistics with PyMC3

Data Visualization

Healy - Data Visualization - A Practical Introduction
Wilke - Fundamentals of Data Visualization
Wickham - ggplot2: Elegant Graphics for Data Analysis, 2nd Ed.
Dougherty - Hands-On Data Visualization

Machine Learning

Daume’ III - A Course in Machine Learning
Simeone - A Brief Introduction to Machine Learning for Engineers
Google Machine Learning Crash Course
Parr et al. - The Mechanics of Machine Learning
Barber - Bayesian Reasoning and Machine Learning
Winn et al. - Model-Based Machine Learning
Kriesel - A Brief Introduction to Neural Networks
Molnar - Interpretable Machine Learning
Battiti et al. - The LION Way. Machine Learning plus Intelligent Optimization
Burkov - The Hundred Pages Machine Learning Book
Bobra - Machine Learning, Statistics, and Data Mining for Heliophysics
Bishop - Pattern Recognition and Machine Learning
Deisenroth et al. - Mathematics for Machine Learning
Rasmussen - Gaussian Processes for Machine Learning
Murphy - Probabilistic Machine Learning: An Introduction
Thakur - Approaching (Almost) Any Machine Learning Problem
Hardt et al. - Pattern, Predictions, and Actions
Treveil et al. - Introducing MLOp
Azencott - Introduction au Machine Learning
Mohri et al. - Foundations of Machine Learning, 2nd Ed.
Gerrish - How Smart Machines Think
James et al. - An Introduction to Statistical Learning, 2nd Ed.
Hastie et al. - The Elements of Statistical Learning, 2nd Ed.

Geographic Information Systems

Lovelace et al. - Geocomputation with R
Bartlein - Geographic Data Analysis (with R)
Donnelly et al. - Introduction to GIS with QGIS
Hijmans - Spatial Data Science with R
Pebesma et al. - Spatial Data Science with Applications in R
QGIS - A Gentle Introduction to GIS
Saylor Academy - Essential of Geographic Information Systems
Rey et al. - Geographic Data Science with PySAL and the PyData Stack
Vermeer - Geodesy
Vermeer - Physical Geodesy
Vermeer - Methods of Navigation
Vermeer et al. - Digital Aerial Mapping: A Hands-On Course
Gimond - Introduction to GIS and Spatial Analysis (with R)

Genetic Programming

Poli - A Field Guide to Genetic Programming

Information Theory and Digital Signal Processing

MacKay - Information Theory, Inference, and Learning Algorithms
Smith - The Scientist and Engineer’s Guide to Digital Signal Processing
Vetterli - Foundation of Signal Processing
Prandoni et al. - Signal Processing for Communications
Peyre’ et al. - Computational Optimal Transport
Frank - Control Theory Tutorial
Francis - Classical Control
Kak et al. - Principles of Computerized Tomographic Imaging

Computer Vision and Computer Graphics

Szeliski - Computer Vision: Algorithms and Applications, 2nd Ed.
Prince - Computer Vision: Models, Learning and Inference
Hyvärinen et al. - Natural Image Statistics
Solem - Programming Computer Vision with Python
Pharr et al. - Physically Based Rendering: From Theory to Implementation, 3rd Ed.
Eck et al. - Introduction to Computer Graphics
Krig - Computer Vision Metrics

Information Retrieval

Manning et al. - Introduction to Information Retrieval

Natural Language Processing

Jurafsky et al. - Speech and Language Processing, 3r Ed. draft
Silge et al. - Text Mining with R
Bird et al. - Natural Language Processing with Python
Eisenstein - Natural Language Processing

Neural Networks, Deep Learning, and Reinforcement Learning

Goodfellow et al. - Deep Learning Book
Karpathy - CS231n Convolutional Neural Network for Visual Recognition
Rojas - Neural Networks - A Systematic Introduction
Hagan et al. - Neural Network Design, 2nd edition
Zhang et al. - Dive into Deep Learning
Hamilton - Graph Representation Learning
Howard et al. - Practical Deep Learning for Coders
Ma et al. - Deep Learning on Graphs
LeCun et al. - Deep Learning
Stevens et al. - Deep Learning with PyTorch
Bronstein et al. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Roberts et al. - The Principles of Deep Learning Theory
Sutton et al. - Reinforcement Learning, 2nd Ed.
Francois-Lavet et al. - An Introduction to Deep Reinforcement Learning
Nielsen - Neural Networks and Deep Learning

Artificial Intelligence and Robotics

Lynch et al. - Modern Robotics - Mechanics, Planning, and Control
Murray et al. - A Mathematical Introduction to Robotic Manipulation
Nilsson - The Quest for Artificial Intelligence: A History of Ideas and Achievements

Databases

Abiteboul - Foundation of Databases
Robinson et al. - Graph Databases

Software Engineering

Felleisen et al. - How to Design Programs
The Architecture of Open Source Applications
Adkins et al. - Building Secure and Reliable Systems

Operating Systems

Arpaci-Dusseau et al. - Operating Systems: Three easy Pieces
Cooper - Advanced Bash-Scripting Guide

Knowledge Representation, Semantic Web, and Linked Data

Bergman - A Knowledge Representation Practionary
Heath et al. - Linked Data: Evolving the Web into a Global Data Space
Dodds et al. - Linked Data Patterns
Hogan et al. - Knowledge Graphs

Information Security

Anderson - Security Engineering, 2nd Edition
Nakov - Practical Cryptography for Developers
Van Houtven - Crypto 101
Boneh et al. - A Graduate Course in Applied Cryptography
Menezes - Handbook of Applied Cryptography
Kohel - Cryptography

Mathematics

Russell - The Principles of Mathematics
Jarvis et al. - Foundations of Applied Mathematics
Law et al. - Data Assimilation: A Mathematical Introduction
Lehman et al. - Mathematics for Computer Science
Gallier et al. Logic and Computation (Topology, Algebra, etc.)
Magnus - forallX - An Introduction to Formal Logic
Abelson et al. - Turtle Geometry: The Computer as a Medium for Exploring Mathematics

Algebra

Ström et al. - Immersive Linear Algebra
Thomas - Computational Linear Algebra for Coders
Cherney et al. - Linear Algebra
Treil - Linear Algebra Done Wrong
Boyd et al. - Introduction to Applied Linear Algebra - Vectors, Matrices, and Least Squares
Ash - Abstract Algebra: The Basic Graduate Year
O’Donnell - Analysis of Boolean Functions

Calculus

Keisler - Elementary Calculus: An Infinitesimal Approach
Axler - Measure, Integration & Real Analysis
Sussman et al. - Functional Differential Geometry

Topology

Ghrist - Elementary Applied Topology
Morris - Topology without tears
Hatcher - Algebraic Topology

Probability and Statistics

Gelman et al. - Bayesian Data Analysis, 3rd edition
Lattimore et al. - Bandit Algorithms
Owen - Monte Carlo theory, methods and examples
Vershynin - High-Dimensional Probability
Clyde et al. - An Introduction to Bayesian Thinking
Blitzstein - Introduction to Probability, 2nd Ed.
Kadane - Principles of Uncertainty, 2nd Ed.
Cranmer - Statistics and Data Science
Davidson-Pilon - Probabilistic Programming and Bayesian Methods for Hackers
Goodman et al. - Probabilistic Models of Cognition
Tanizaky - Computational Methods in Statistics and Econometrics
Johnson - Bayes Rules ! An Introduction to Bayesian Modeling with R
Hyndman et al. - Forecasting: Principles and Practice, 3rd Ed.

Causal Inference

Pearl - Causality
Hernan - Causal Inference Book
Peters - Elements of Causal Inference
Sharma et al. - Causal Reasoning: Fundamentals and Machine Learning Applications
Facure Alves - Causal Inference for the Brave and True

Number Theory

Stein - Elementary Number Theory: Primes, Congruences, and Secrets
Shoup - A Computational Introduction to Number Theory and Algebra, 2nd Ed.

Optimization and Linear Programming

Dantzig - Linear Programming and Extensions
Vishnoi - Algorithms for Convex Optimization

Graph Theory

Barabasi - Network Science
Easley et al. - Network, Crowds, and Markets: Reasoning About a Highly Connected World
Lyons et al. - Probability on Trees and Networks
Guichard - An Introduction to Combinatorics and Graph Theory
Williamson - Network Flow Algorithms
Lovasz - Large networks and graph limits
van Steen - Graph Theory and Complex Networks

Game Theory

Dresher - Games of Strategy - Theory and Applications
Shoham - Multiagent Systems
Karlin - Game Theory, Alive

Category Theory

Milewski - Category Theory for Programmers
Fong et al. - Seven Sketches in Compositionality: An Invitation to Applied Category Theory
Leinster - Basic category Theory

Finance and Economics

Welch - Corporate Finance, 4th Edition
Heller - The Birth of Capitalism: A 21st Century Perspective
Lengwiler - Microfoundations of Financial Economics - An Introduction to General Equilibrium Asset Pricing

Physics

Geophysics and Remote Sensing

Mathematical and Computational Physics

Quantum Mechanics and Quantum Computing

Statistical Mechanics

Nuclear Physics

Biology

Computer Science

Algorithms and Data Structures

Distributed Systems and High Performance Computing

Computer Programming

R

JavaScript

Haskell

Scala

Java

Data Science

Data Visualization

Machine Learning

Geographic Information Systems

Genetic Programming

Information Theory and Digital Signal Processing

Computer Vision and Computer Graphics

Information Retrieval

Natural Language Processing

Neural Networks, Deep Learning, and Reinforcement Learning

Artificial Intelligence and Robotics

Databases

Software Engineering

Operating Systems

Knowledge Representation, Semantic Web, and Linked Data

Information Security

Mathematics

Algebra

Calculus

Topology

Probability and Statistics

Causal Inference

Number Theory

Optimization and Linear Programming

Graph Theory

Game Theory

Category Theory

Finance and Economics