mathematics for computer science book
Information Technology / November 9, 2017

mathematics for computer science book is written by Eric Lehman, F Thomson Leighton, and Albert R Meyer. This book contains 1006 pages and consists of 5 sections. This book  have 22 chapters starting from What is a Proof?, The Well Ordering Principle, Logical Formulas, Mathematical Data Types, Induction, State Machines,  Recursive Data Types, Infinite Sets,  Number Theory, Directed graphs & Partial Orders, Communication Networks, Simple Graphs, Planar Graphs, Sums and Asymptotics, Cardinality Rules, Generating Functions, Events and Probability Spaces,  Conditional Probability, Random Variables, Deviation from the Mean,  Random Walks, and Recurrences. This book is licensed under creative common attribution-sharealike 3.0 licensed. About the Authors of mathematics for computer science book: Eric D. Lehman, teaches travel literature and creative writing at the University of Bridgeport, and his essays, reviews, and stories have appeared in dozens of journals and magazines. Dr. Tom Leighton co-founded Akamai Technologies in 1998 and served as Akamai’s Chief Scientist until he became its CEO in 2013. Under Dr. Leighton’s leadership, Akamai has evolved from its origins as a Content Delivery Network (CDN) into one of the most essential and trusted cloud delivery and cybersecurity platforms, upon which many of the world’s best brands and enterprises build and secure their digital experiences. During his initial four years as CEO, Akamai’s revenue and profit grew by 70%, and annual…

introduction to information retrieval pdf
Information Technology / November 9, 2017

introduction to information retrieval pdf is the book written by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze.  This book consists of 581 pages and contains 21 chapters starting from Boolean retrieval,  The term vocabulary and postings lists, Dictionaries and tolerant retrieval,  Index construction, Index compression,  Scoring, term weighting and the vector space model,  Computing scores in a complete search system, Evaluation in information retrieval, Relevance feedback and query expansion,  XML retrieval, Probabilistic information retrieval, Language models for information retrieval,  Text classification and Naive Bayes, Vector space classification, Support vector machines and machine learning on documents, Flat clustering,  Hierarchical clustering, Matrix decompositions and latent semantic indexing,  Web search basics,  Web crawling and indexes, and Link analysis. This book is published in april 1, 2009 under the Cambridge university press. About the Authors of introduction to information retrieval pdf: Christopher Manning is the inaugral Thomas M. Siebel Professor in Machine Learning in the Departments of Computer Science and Linguistics at Stanford University. His research goal is computers that can intelligently process, understand, and generate human language material. Manning is a leader in applying Deep Learning to Natural Language Processing, with well-known research on Tree Recursive Neural Networks, sentiment analysis, neural network dependency parsing, the GloVe model of word vectors, neural machine translation, and deep language understanding. He…