Bookmarks on Ebooks
Ebook
Free Ebook
Free Book on Neural Network (Artificial Intelligence)
Neural Nets, Kevin Gurney
An Introduction to Artificial Neural Networks, C.A.L. Bailer-Jones berg, R. Gupta, H.P. Singh
Neural Networks, Genevieve Orr
Machine Learning, Neural and Statistical Classification, D. Michie, D.J. Spiegelhalter, C.C. Taylor
Planning Algorithms, Steven M. LaValle
Introduction to Machine Learning, Nils J. Nilsson
Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto
An Introduction to Neural Networks Ben Krose, Patrick van der Smagt
Neural Networks - A Systematic Introduction, Raul Rojas
Neural Networks, Christos Stergiou and Dimitrios Siganos
Dynamics of Complex Systems, Yaneer Bar-Yam
Convex Optimization, Stephen Boyd and Lieven Vandenberghe
Reinforcement Learning:An Introduction, Richard S. Sutton, Andrew G. Barto
Computing and the Brain, Dr Bruce Graham
A Genetic Algorithm Tutorial, Darrell Whitley
Artificial Intelligence through Prolog, Neil C. Rowe
Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K. I. Williams
Global Optimization Algorithms - Theory and Application, Thomas Weise
Introduction to Neural Networks with Java, Jeff Heaton
Prolog and Natural-Language Analysis, Fernando C. N. Pereira, Stuart M. Shieber
Fundamentals of Wireless Communication, David Tse and Pramod Viswanath
Information Theory, Inference and Learning Algorithms, David J. C. MacKay
Entropy and Information Theory****, R.M. Gray
Complexity Issues in Coding Theory, Alexander Barg
Network Coding Theory, Raymond W. Yeung, Shuo-Yen Robert Li, Ning Cai and Zhen Zhang
Notes on Coding Theory, Jonathan I. Hall
Theory of Codes, Jean Berstel, Dominique Perrin, C. Reutenauer
Codes and Automata, Jean Berstel, Dominique Perrin, C. Reutenauer
A Short Course in Information Theory, David J.C. MacKay
Information, Randomness and Incompleteness, G J Chaitin, IBM Research
A Discipline Independent Definition of Information, Robert M. Losee
A Mathematical Theory of Communication, Claude E. Shannon
UWB Communication Systems—A Comprehensive Overview, Edited by: Maria-Gabriella Di Benedetto, Thomas Kaiser, Andreas F.Molisch, Ian Oppermann, Christian Politano, and Domenico Porcino
Introduction to Data Communications, by Eugene Blanchard
Asterisk: The Future of Telephony, Jim Van Meggelen/Jared Smith/Leif Madsen
Primer on Information Theory, Thomas Schneider
A Discipline Independent Definition of Information, Robert M. Losee
High-Speed Communication Circuits and Systems, Prof. Michael Perrott
Communication System Design, Prof. Vladimir Stojanovic
Essential Coding Theory, Prof. Madhu Sudan
Speech Communication, Prof. Kenneth Steven
Quantum Optical Communication, Prof. Jeffrey H. Shapiro
Principles of Wireless Communications, Prof. Lizhong Zheng
Principles of Digital Communications I, Prof. Robert Gallager, Prof. Lizhong Zheng
Principles of Digital Communication II, Prof. David Forney
Quantum Information Science, Prof. Issac Chuang, Prof. Peter Shor
Transmission of Information, Prof. Muriel Medard, Prof. Lizhong Zheng
Data Communication Networks, Prof. Eytan Modiano
Stochastic Processes, Detection, and Estimation, Prof. Alan Willsky, Prof. Gregory Wornell
Primer on Information Theory by Thomas Schneider
Stochastic Processes, Detection and Estimation-A. S. Willsky and G. W. Wornell
eBook
Data Journalism Handbook 2–Online beta access to the first 21 chapters
Select Star SQL–A book that is also a walk-through interactive tutorial for learning SQL
Dive Into Deep Learning–A very detailed and up-to-date book on Deep Learning; used at Berkeley. It also includes Jupyter notebooks.
R for Data Science–Just like the title says, learn to use R for data science.
Advanced R–A work in progress for the second edition of the book.
Foundations of Data Science–Free Book by Avrim Blum, John Hopcroft, and Ravindran Kannan wrote the book, Foundations of Data Science (PDF download).
Introduction to Probability by Joseph Blitzstein and Jessica Hwang is available as a free PDF on Google Docs.
Elements of Data Science–A free Jupyter Notebook Textbook Elements of Data Science by Allen Downey is a freely available textbook.
Free Reinforcement Learning Textbook. Reinforcement Learning: An Introduction by Rich Sutton and Andrew Barto. The full text is available on a Google Drive at Reinforcement Learning.
Pablo Casas has published a book freely available online, Data Science Live Book.
Model-Based Machine Learning–Chapters of this book become available as they are being written. It introduces machine learning via case studies instead of just focusing on the algorithms.
Foundations of Data Science–This is a much more academic-focused book which could be used at the undergraduate or graduate level. It covers many of the topics one would expect: machine learning, streaming, clustering and more.
Deep Learning Book–This book was previously available only in HTML form and not complete. Now, it is free and downloadable.
Professor Norm Matloff from the University of California, Davis has published From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science which is an open textbook.
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Associate Professor at the School of Computer Science and Engineering at The Hebrew University, Israel.
Hal Daumé III, Assistant Professor of Computer Science at the University of Maryland, has placed the contents of his book online. The book is titled A Course in Machine Learning.
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