كوكب الجغرافيا ديسمبر 05, 2019 ديسمبر 05, 2019
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  1. 10 Print – Free ebook from MIT Press about Commodore 64 BASIC
  2. A Byte of Python
  3. A Computer Science Tapestry: Exploring Programming and Computer Science with C++ by Astrachan
  4. A Course in Machine Learning
  5. A Field Guide to Genetic Programming
  6. A First Course on Time Series Analysis with Examples in SAS
  7. A Machine Made This Book: Ten Sketches Of Computer Science
  8. A New Kind of Science by Stephen Wolfram
  9. A Pamphlet against R: Computational Intelligence in Guile Scheme
  10. A Practical Introduction to Data Structures and Algorithm Analysis by Clifford A. Shaffer
  11. A Quick and Gentle Guide to Constraint Logic Programming via ECLiPSe by Antoni Niederlinski
  12. Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi
  13. Advances In Genetic Programming 3 by Lee Spector, William B. Langdon, Una-May O’Reilly and Peter J. Angeline
  14. Algorithmic Mathematics
  15. Algorithms and Data Structures for External Memory (Series on Foundations and Trends in Theoretical Computer Science) by Jeffrey S. Vitter
  16. Algorithms for Clustering Data by Jain and Dubes
  17. Algorithms Illuminated (video book)
  18. Algorithms 4th Edition by Robert Sedgewick and Kevin Wayne
  19. Algorithms by Jeff Erickson
  20. An implementation of J
  21. An Introduction to Functional Programming Through Lambda Calculus/Elementary Standard ML by Greg Michaelson
  22. An Introduction to Probabilistic Programming
  23. Anisotropic Diffusion in Image Processing by Joachim Weickert
  24. Applied Mathematical Programming
  25. Artificial Intelligence: Foundations of Computational Agents by David Poole, Alan Mackworth
  26. ASN.1 Communication between Heterogeneous Systems by Olivier Dubuisson
  27. Assemblers And Loaders
  28. Basic Data Analysis and More: A Guided Tour Using Python
  29. Basics of Compiler Design
  30. Beej’s Guide to Network Programming
  31. Blast from the Past: Unix text Processing
  32. Building Blocks for Theoretical Computer Science by Margaret M. Fleck
  33. C# Yellow Book by Rob Miles
  34. Calculus by Gilbert Strang
  35. Capability Based Computer Systems
  36. Category Theory for Computing Science by Michael Barr and Charles Wells
  37. Category Theory for Programmers by Bartosz Milewski
  38. Certified Programming with Dependent Types
  39. Clean Architectures in Python
  40. Clever Algorithms: Nature-Inspired Programming Recipes
  41. CODD: The relational model for database management
  42. Combinatorial Algorithms 2nd Edition by Herbert Wilf
  43. Combinatorial Optimization: Exact and Approximate Algorithms
  44. Common Lisp: A Gentle Introduction to Symbolic Computation
  45. Communicating Sequential Processes (CSP) by C.A.R. Hoare
  46. Communication Network Analysis
  47. Compiler Construction by Niklaus Wirth
  48. Think Complexity by Allen B. Downey
  49. Computational Statistics with Python (2017 edition)
  50. Computer Organization and Design Fundamentals
  51. Computer Science I
  52. Computer Science: Abstraction to Implementation by Keller
  53. Computer Vision: Algorithms and Applications
  54. Computers and Thought: A practical Introduction to Artificial Intelligence
  55. Computers in Communication by Gordon Brebner
  56. Concrete Abstractions: An Introduction to Computer Science Using Scheme by Hailperin, Kaiser and Knight
  57. Concrete Semantics
  58. Convex Optimization by Stephen Boyd and Lieven Vandenberghe
  59. Crafting Interpreters
  60. Cryptography and Data Security by Denning
  61. Cryptography: An Introduction by Nigel Smart
  62. Data Structures & Algorithm Analysis (Edition 3.2) by Clifford A. Shaffer
  63. Data Structures and Algorithms: The Basic Toolbox by Kurt Mehlhorn,Peter Sanders
  64. Deep Learning by Goodfellow, Bengio, & Courville
  65. Denotational Semantics: A Methodology for Language Development by Schmidt
  66. Design of Approximation Algorithms by David P. Williamson and David B. Shmoys
  67. Designing and Building Parallel Programs
  68. Digraphs: Theory, Algorithms and Applications 1st Edition
  69. Distributed Control of Robotic Networks by Bullo, Cortez, Martinez
  70. Distributed systems for Fun and Profit
  71. Distributed Systems 3rd Edition by Van Steen & Tannenbaum
  72. Eloquent JavaScript
  73. Entropy and Information Theory by Robert M. Gray
  74. Essentials of Metaheuristics
  75. Evolved to Win by Moshe Sipper
  76. F# Succinctly (requires registration)
  77. Finding Source Code on the Web for Remix and Reuse
  78. Forecasting: Principles And Practice
  79. Foundations of Computer Science by Aho and Ullman
  80. Foundations of Databases: A book on design of databases
  81. Foundations of Statistical Natural Language Processing
  82. Free CS articles used at KTH in Stockholm: Basic algorithms, data structures and algorithm analysis, plenty of code examples.
  83. 26 Free Smalltalk ebooks
  84. From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science (HTML)
  85. Functional Programming in OCaml by Michael Clarkson
  86. Game Programming Patterns by Robert Nystrom
  87. Gaussian Processes for Machine Learning by Carl E. Rasmussen, Christopher K. I. Williams
  88. GPU Gems 2: Programming Techniques for High-Performance Graphics
  89. GPU Gems 3: 3D and General Programming Techniques for GPUs
  90. GPU Gems: 3D Programming Techniques, Tips, and Tricks
  91. GPU Gems: Programming Techniques, Tips and Tricks for Real-Time Graphics
  92. Handbook of Applied Cryptography
  93. Haskell Book
  94. Higher-Order Perl by Mark Jason Dominus
  95. Hoare: Essays in computing science
  96. How to Design Programs
  97. How to Think Like a Computer Scientist in Python, Java, and C++
  98. How to Use Scheme
  99. Human JavaScript by Henry Joreteg
  100. Implementing Functional Languages
  101. Implementing Programming Languages
  102. Information Theory, Inference, and Learning Algorithms
  103. Introduction to Computing: Explorations in Language, Logic, and Machines by David Evans
  104. Introduction to Deep Computer Vision
  105. Introduction to Information Retrieval
  106. Introduction to Machine Learning
  107. Introduction to statistical thought by Michael L. Lavine
  108. Introduction to Theory of Computation
  109. Invent with Python
  110. Is Parallel Programming Hard, And What Can You Do About It.
  111. Learn C: Build Your Own Lisp
  112. Learn Prolog Now! by Patrick Blackburn, Johan Bos, and Kristina Striegnitz
  113. Learn Python the Hard Way 3rd Edition by Zed A. Shaw
  114. Learning JavaScript Design Patterns
  115. Let Over Lambda
  116. Linux Device Drivers 3rd Edition
  117. Linux Kernel in a Nutshell by Greg Kroah-Hartman
  118. Logic for Computer Science: Foundations of Automatic Theorem Proving by Gallier
  119. Logic, Programming and Prolog 2nd Edition by Ulf Nilsson and Jan Maluszynski
  120. Machine Learning, Neural and Statistical Classification by Michie, Spiegelhalter and Taylor
  121. Math and Computation by Avi Wigderson
  122. Mathematics for Computer Science by Lehman & Leighton
  123. Mathematics for Computer Science by Eric Lehman, F. Thomson Leighton, Albert R. Meyer (CCBYNCSA)
  124. Matters Computational formerly Algorithms for Programmers by Jörg Arndt
  125. Mercurial: The Definitive Guide
  126. Prolog Programming in Depth and Natural Language Processing for Prolog Programmers by Michael A. Covington
  127. Mining of Massive Datasets
  128. MMURTL V1.0 aka Developing Your own 32 Bit Operating System
  129. Modern Computer Arithmetic
  130. Most Influential Books for Programmers
  131. Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
  132. Natural Image Statistics: A Probabilistic Approach to Early Computation Vision by Hyvärinen, Hurri and Hoyer
  133. Natural Language Processing Techniques in Prolog by Patrick Blackburn and Kristina Striegnitz
  134. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper
  135. Nature of Code
  136. Networks, Crowds, and Markets: Reasoning about a Highly Connected World by Easley, Kleinberg
  137. Neural Network Design
  138. Neural Networks – A Systematic Introduction
  139. Neural Networks and Deep Learning
  140. Node.js Succinctly by Emanuele DelBono
  141. Non-Uniform Random Variate Generation
  142. Notes on Theory of Distributed Systems (Yale CPSC 465/565: Fall 2017 Course Notes)
  143. Open Government by Aaron Swartz
  144. O’Reilly’s Real World OCaml
  145. Object-oriented Programming in Javaâ„¢
  146. Object-Oriented Programming with ANSI-C
  147. Object-Oriented Reengineering Patterns
  148. On Lisp: A Comprehensive Study of Advanced Lisp Techniques
  149. Open Data Structures by Pat 
  150. Operating Systems and Middleware: Supporting Controlled Interaction by Max Hailperin
  151. Operating Systems: Three Easy Pieces
  152. Optimized Numerical Algorithms Book and Implementations
  153. Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig
  154. Parallel and Distributed Computation: Numerical Methods by Dimitri P. Bertsekas and John Tsitsiklis. Athena Scientific
  155. Partial Evaluation and Automatic Program Generation by Jones, Gomard, Sestoft
  156. PC Assembly
  157. Perl 6 at a Glance by Andrew Shitov
  158. Peter Shirley’s Ray Tracing Books
  159. Physically Based Rendering: From Theory to Implementation
  160. Planning Algorithms / Motion Planning by Steven M. LaValle
  161. Porting UNIX Software
  162. Practical Common Lisp
  163. Practical File System Design with the Be File System by Dominic Giampaolo
  164. Concepts and Applications of Inferential Statistics by Richard Lowry
  165. Principles of Computer System Design: An Introduction
  166. Principles of Distributed Computing by Roger Wattenhofer
  167. Probabilistic Models of Cognition
  168. Problem Solving with Algorithms and Data Structures using Python
  169. Producing Open Source Software
  170. Professor Frisby’s Mostly Adequate Guide to Functional Programming
  171. Programming and Programming Languages
  172. Programming Books for Professionals
  173. Programming from the Ground Up
  174. Programming in D by Ali Çehreli
  175. Programming in Lua 1st Edition
  176. Programming Languages: Application and Interpretation by Shriram Krishnamurthi
  177. Programming on Parallel Machines – GPU, Multicore, Clusters and More
  178. Proofs and Types
  179. Purely Functional Data Structures
  180. Python 3 Patterns, Recipes and Idioms
  181. Python Data Science Handbook
  182. Quantitative System Performance: Computer System Analysis Using Queueing Network Models
  183. Readings in Database Systems 5th Edition
  184. Reinforcement Learning And Optimal Control
  185. Scratchapixel: Learn Computer Graphics From Scratch!
  186. Do It Yourself Agile 2nd Edition
  187. Security Engineering by Anderson
  188. Semantic Mining of Social Networks
  189. ShaderX Books : ShaderX, ShaderX2: Intro & Tutorials, Tips & Tricks by Engel
  190. SICP 2nd Edition
  191. Simply Scheme: Introducing Computer Science 2nd Edition by Brian Harvey, Matthew Wright
  192. Software Design Using C++ by Br. David Carlson
  193. Software Engineering for Internet Applications by Andersson, Greenspun, Grumet
  194. 4 Volumes of Software Foundations
  195. Specification Case Studies 2nd Ed by Ian Hayes
  196. Speech and Language Processing by Jurafsky, Martin
  197. Stack Computers: The New Wave by Philip J. Koopman, Jr.
  198. Stanford CS Book: Mining of Massive Datasets by Rajaraman, Ullman
  199. Starting Forth by Leo Brodie
  200. Strange Attractors: Creating Patterns in Chaos by Sprott
  201. Successful Lisp
  202. Reinforcement Learning: An Introduction by Sutton & Barto
  203. Syncfusion Series of E-books (Assembly, C++, ASP.NET, Data Structures, etc)
  204. Teach Yourself Scheme in Fixnum Days by Dorai Sitaram
  205. Text Algorithms by M. Crochemore / W. Rytter
  206. The Algorithmic Beauty of Plants by Przemyslaw Prusinkiewicz and Aristid Lindenmayer
  207. The Ancient Art of the Numerati: A Programmer’s Guide to Data Mining)
  208. The Architecture of Open Source Applications
  209. The Art of Unix Programming
  210. The C Book
  211. The Computer Revolution In Philosophy: Philosophy, Science and Models of Mind
  212. The Craft of Programming by Reynolds
  213. The Craft of Text Editing
  214. The Debian Administrator’s Handbook
  215. The Design and Implementation of Probabilistic Programming Languages by N. D. Goodman and A. Stuhlmüller
  216. The Design of Approximation Algorithms
  217. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  218. The Haskell Road to Logic, Math and Programming by Doets and van Eijck
  219. The HoTT Book | Homotopy Type Theory
  220. The Hundred-Page Machine Learning Book by Andriy Burkov
  221. The Implementation of Functional Programming Languages, by Simon Peyton Jones
  222. The Internals of PostgreSQL
  223. The Linux Command Line by William Shotts
  224. The Little 
  225. operating system
  226. The Matrix Calculus You Need for Deep Learning by Terence Parr and Jeremy Howard
  227. The OpenGL Programming Guide by The Redbook
  228. The Playful Machine: Theoretical Foundation and Practical Realization of Self-Organizing Robots
  229. The Power of Prolog
  230. The Quest for Artificial Intelligence – A History of Ideas and Achievements – by Nils J. Nilsson (Stanford University)
  231. The Scheme Programming Language, 4th Edition
  232. The Scientist and Engineer’s Guide to Digital Signal Processing by Dr. Steven W. Smith
  233. The Theory and Practice of Concurrency by A. W. Roscoe
  234. The Ultimate Question of Programming, Refactoring, and Everything
  235. The Way To Go: A Thorough Introduction to the Go Programming Language
  236. Think Bayes: Bayesian Statistics Made Simple – Allen B. Downey
  237. Think DSP – Digital Signal Processing in Python
  238. Think Stats: Probability and Statistics for Programmers
  239. Thinking Forth
  240. Type Theory and Functional Programming
  241. Understanding and Writing Compilers – Richard Bornat
  242. UNIX Text Processing
  243. Using Z: Specification, Refinement, and Proof (Formal techniques and formal methods for software engineering)
  244. Vector Models for Data-Parallel Computing – Guy Blelloch
  245. VT330/VT340 Programmer Reference Manual – Volume 2: Graphics Programming
  246. Web Data Management (Abiteboul, Manolescu, Rigaux, Rousset, & Senellart. Cambridge University Press, 2011)
  247. What the C or C++ Programmer Needs to Know About C# and the .NET Framework – Charles Petzold
  248. xv6 – a simple, Unix-like teaching operating system

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