Machine Learning – A Probabilistic Perspective

About The E-Book

• Title – Pattern Recognition and Machine Learning
• Author – Kevin P. Murphy
• Total Pages – 1098 Pages
• Total Chapters – 28 Chapters
• E-Book Size – 26 MBs

Today’s Web-enabled electronic data deluge calls for automated data analysis methods. Machine learning provides these, developing methods that can automatically detect patterns in the data and then use the uncovered ways to predict future data. This textbook offers a self-contained introduction to machine learning based on a unified approach.

The coverage combines breadth and depth, offering the necessary background material on such topics as probability, optimization, and linear algebra, as well as a discussion of the recent developments in the field, including conditional random domains, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with the code for the most important algorithms.

Book Contents

Download “Machine Learning – A Probabilistic Perspective” Machine-Learning_-A-Probabilistic-Perspective (Mr Techies).pdf – Downloaded 6 times – 25.69 MB

Machine Learning – Example Pages

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