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Over the 25 years of its existence, the book has become a near-classic; it is now in its third edition, including additional topics such as types and modules. Its first part now incorporates ideas on programming from HtDP, another unconventional textbook, which uses Scheme to teach the principles of program design.
Preface The third edition was significantly changed, adding a section on learning, including neural networks. The third edition was also updated to reflect changes in computer performance that had occurred since the second edition was published, and to address artificial intelligence at scale. [ 3 ] :
MIT Press published the first edition in 1984, and the second edition in 1996. It was formerly used as the textbook for MIT's introductory course in computer science. SICP focuses on discovering general patterns for solving specific problems, and building software systems that make use of those patterns. [2]
MIT Press is a distributor for Semiotext(e), Goldsmiths Press, Strange Attractor Press, Sternberg Press, Terra Nova Press, Urbanomic, and Sequence Press. In 2000, the MIT Press created CogNet, an online resource for the study of the brain and the cognitive sciences. [7] In 1981, MIT Press published its first book under the Bradford Books ...
Introduction to Algorithms is a book on computer programming by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein.The book is described by its publisher as "the leading algorithms text in universities worldwide as well as the standard reference for professionals". [1]
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The Computer Contradictionary is a non-fiction book by Stan Kelly-Bootle that compiles a satirical list of definitions of computer industry terms. It was originally published as 'The devil's DP dictionary'. it is an example of "cynical lexicography" in the tradition of Ambrose Bierce's The Devil's Dictionary. [1]
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
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