Bookmarks on Ebooks

Ebook

• Top Hacker News Books of All Time

• Introduction to Information Retrieval

• Plagiarism Checker - Graduateway

• Ebookee: Free Download eBooks Search Engine!

• hypertextbook

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

1. Introduction to Neural Networks

2. Machine Learning, Neural and Statistical Classification, D. Michie, D.J. Spiegelhalter, C.C. Taylor

3. Planning Algorithms, Steven M. LaValle

4. Introduction to Machine Learning, Nils J. Nilsson

5. Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto

6. An Introduction to Neural Networks Ben Krose, Patrick van der Smagt

7. Neural Networks - A Systematic Introduction, Raul Rojas

8. Neural Networks, Christos Stergiou and Dimitrios Siganos

9. Dynamics of Complex Systems, Yaneer Bar-Yam

10. Convex Optimization, Stephen Boyd and Lieven Vandenberghe

11. Reinforcement Learning:An Introduction, Richard S. Sutton, Andrew G. Barto

12. Computing and the Brain, Dr Bruce Graham

13. A Genetic Algorithm Tutorial, Darrell Whitley

14. Artificial Intelligence through Prolog, Neil C. Rowe

15. Brief Introduction to Educational Implications of Artificial Intelligenc, David Moursund

16. Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K. I. Williams

17. Global Optimization Algorithms - Theory and Application, Thomas Weise

18. Introduction to Neural Networks with Java, Jeff Heaton

19. Practical Artificial Intelligence Programming in Java, Mark Watson

20. Prolog and Natural-Language Analysis, Fernando C. N. Pereira, Stuart M. Shieber

Free Books on Information Theory and Communication System

1. Fundamentals of Wireless Communication, David Tse and Pramod Viswanath
2. An Introduction to Wireless Technology, IBM
3. Information Theory, Inference and Learning Algorithms, David J. C. MacKay
4. Entropy and Information Theory****, R.M. Gray
5. Complexity Issues in Coding Theory, Alexander Barg
6. Network Coding Theory, Raymond W. Yeung, Shuo-Yen Robert Li, Ning Cai and Zhen Zhang
7. Notes on Coding Theory, Jonathan I. Hall
8. Theory of Codes, Jean Berstel, Dominique Perrin, C. Reutenauer
9. Codes and Automata, Jean Berstel, Dominique Perrin, C. Reutenauer
10. A Short Course in Information Theory, David J.C. MacKay
11. Information, Randomness and Incompleteness, G J Chaitin, IBM Research
12. A Discipline Independent Definition of Information, Robert M. Losee
13. A Mathematical Theory of Communication, Claude E. Shannon
14. The Limits of Mathematics: A Course on Information Theory and the Limits of Formal Reasoning , G J Chaitin
15. UWB Communication Systems—A Comprehensive Overview, Edited by: Maria-Gabriella Di Benedetto, Thomas Kaiser, Andreas F.Molisch, Ian Oppermann, Christian Politano, and Domenico Porcino
16. Introduction to Data Communications, by Eugene Blanchard
17. Understanding Optical Communications
18. Asterisk: The Future of Telephony, Jim Van Meggelen/Jared Smith/Leif Madsen
19. Primer on Information Theory, Thomas Schneider
20. A Discipline Independent Definition of Information, Robert M. Losee
21. High-Speed Communication Circuits and Systems, Prof. Michael Perrott
22. Communication System Design, Prof. Vladimir Stojanovic
23. Essential Coding Theory, Prof. Madhu Sudan
24. Speech Communication, Prof. Kenneth Steven
25. Quantum Optical Communication, Prof. Jeffrey H. Shapiro
26. Principles of Wireless Communications, Prof. Lizhong Zheng
27. Principles of Digital Communications I, Prof. Robert Gallager, Prof. Lizhong Zheng
28. Principles of Digital Communication II, Prof. David Forney
29. Quantum Information Science, Prof. Issac Chuang, Prof. Peter Shor
30. Transmission of Information, Prof. Muriel Medard, Prof. Lizhong Zheng
31. Data Communication Networks, Prof. Eytan Modiano
32. Stochastic Processes, Detection, and Estimation, Prof. Alan Willsky, Prof. Gregory Wornell
33. Primer on Information Theory by Thomas Schneider
34. Stochastic Processes, Detection and Estimation-A. S. Willsky and G. W. Wornell

eBook

• Learn Python the Hard Way
• 22 Free Data Science Books
• Welcome · Advanced R.
• PH525x series - Biomedical Data Science
• Neural networks and deep learning
• Mining of Massive Datasets
• NLTK Book
• 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.