Information Theory, Inference and Learning Algorithms Review

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(More customer reviews)Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.
This is _not_ just a book for the experts. However, you will need to think and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

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Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way.In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

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Gateway to Memory: An Introduction to Neural Network Modeling of the Hippocampus and Learning (Issues in Clinical and Cognitive Neuropsychology) Review

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(More customer reviews)To those students, mostly at the undergraduate level, that are not applied mathematic specialists and have looked for an easy to understand, well written, introduction to Neural Networks in learning processes; this is a must-have book. "Gateway..." is an excellent read, targeted to readers from a wide scope of backgrounds, from Biologists to Computer Science Majors through Medical Sciences.
Although leaning a bit too heavily on the conceptual view of the subject, the book introduces the reader gradually to what a Neural Network is, what a computer model is and how it works.
Two basic tools are of special interest to the reader. First, the beginning half of the book gives a general, yet very complete, introduction of the concepts, history and theory to be used. Second, for those with a little more interest in the mathematics there's a good number of "Math Boxes" delving into the details of the subject(s).
The effect of these tools is that, by the time you reach into the core matter of how neural networks models are constructed and worked in the field of Hippocampal memory, the reader suddenly finds his/herself well-familiarized with the theory. A comfortable, gradual, learning process has taken place without any discomfort (and in less time than you would think!).

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