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Computational geometry : an introduction / Franco P Preparata; Michael Ian Shamos
New York : Springer-Verlag, 1985
398 pages. : illustrations ; 24 cm.
Ký hiệu phân loại (DDC): 004.0151
A fundamental task of computational geometry is identifying concepts, properties and techniques which help efficient algorithmic implementations for geometric problems. The approach taken here is the presentations of algorithms and the evaluation of their worst case complexity. The particular problems addressed include geometric searching and retrieval, convex hull construction and related problems, proximity, intersection and the geometry of rectangles.
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3
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Introduction to data compression / Khalid Sayood
Amsterdam ; Boston : Elsevier, 2006
703 p. : illustrations
Ký hiệu phân loại (DDC): 005.746
Each edition of Introduction to Data Compression has widely been considered the best introduction and reference text on the art and science of data compression, and the third edition continues in this tradition. Data compression techniques and technology are ever-evolving with new applications in image, speech, text, audio, and video. The third edition includes all the cutting edge updates the reader will need during the work day and in class. Khalid Sayood provides an extensive introduction to the theory underlying todays compression techniques with detailed instruction for their applications using several examples to explain the concepts. Encompassing the entire field of data compression Introduction to Data Compression, includes lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, scalar and vector quantization. Khalid Sayood provides a working knowledge of data compression, giving the reader the tools to develop a complete and concise compression package upon completion of his book. * New content added on the topic of audio compression including a description of the mp3 algorithm * New video coding standard and new facsimile standard explained * Completely explains established and emerging standards in depth including JPEG 2000, JPEG-LS, MPEG-2, Group 3 and 4 faxes, JBIG 2, ADPCM, LPC, CELP, and MELP * Source code provided via companion web site that gives readers the opportunity to build their own algorithms, choose and implement techniques in their own applications.
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4
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Introduction to deep learning : from logical calculus to artificial intelligence / Sandro Skansi
Cham, Switzerland : Springer, 2018.
196 pages. : illustrations
Ký hiệu phân loại (DDC): 006.312
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: Introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning Discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network Examines convolutional neural networks, and the recurrent connections to a feed-forward neural network Describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning Presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology. Dr. Sandro Skansi is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia.
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