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Compilers: Principles, Techniques, and Tools [1] is a computer science textbook by Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman about compiler construction for programming languages. First published in 1986, it is widely regarded as the classic definitive compiler technology text. [2]
The magazine said that the book was not easy to read, but that it would expose experienced programmers to both old and new topics. [8] A review of SICP as an undergraduate textbook by Philip Wadler noted the weaknesses of the Scheme language as an introductory language for a computer science course. [9]
Download as PDF; Printable version; In other projects Wikidata item; Appearance. move to sidebar hide. ... Computer science textbooks (1 C, 3 P) Computer security ...
English: This textbook consists of notes for the CSci 1001 Overview of Computer Science class at the University of Minnesota-Twin Cities. More information about that class and these notes are in the opening chapter.
The first volume of ‘The Art of Computer Programming’, ‘Fundamental Algorithms’, took five years to complete between 1963 and 1968 while working at both Caltech and Burroughs. Knuth's dedication in Volume 1 reads: This series of books is affectionately dedicated to the Type 650 computer once installed at Case Institute of Technology,
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]
Publishers Weekly, shortly after Code's publication, said "Initial response, at least among traditional tech book readers, has been positive" and quotes the book's editor, Ben Ryan, as saying "We're trying to cross the boundary of the computer section, and break out Code as general nonfiction science". It also praises both the quality of the ...
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 computer vision. [7]