• Title Data Compression Explained
• Author(s) Matt Mahoney
• Publisher: Dell, Inc.
• Paperback: N/A
• eBook HTML, ePub, Mobi
• Language: English
• ISBN-10/ASIN: N/A
• ISBN-13: N/A
• Share This:  

Book Description

Data compression is the art of reducing the number of bits needed to store or transmit data. Prior programming ability and some math skills will be needed.

This book provides an extensive introduction to the theory underlying today's compression techniques with detailed instruction for their applications using several examples to explain the concepts. Encompassing the entire field of data compression, it includes lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, scalar and vector quantization.

It also provides a working knowledge of data compression, giving the reader the tools to develop a complete and concise compression package upon completion of the book.

• N/A

• Amazon

• Data Storage, Data Warehouse, Data Lake, etc.
• Data Analysis and Data Mining
• Big Data
• Data Science

• Data Compression Explained (Matt Mahoney)
• The Mirror Site (1) - PDF

• Foundations of Machine Learning (Mehryar Mohri, et al)

  This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

• Machine Learning Yearning (Andrew Ng)

  You will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

• Understanding Machine Learning: From Theory to Algorithms

  Explains the principles behind the automated learning approach and the considerations underlying its usage. Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

• Reinforcement Learning: An Introduction, Second Edition

  It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

• Probabilistic Machine Learning: An Introduction (Kevin Murphy)

  This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.