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Mastering Probabilistic Graphical Models using Python

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关注 过往记忆大数据 微信公众号,回复 3906 获取本书下载地址。

子标题:Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python

Mastering Probabilistic Graphical Models using Python
作者:
Ankur Ankan
ISBN-10:
1784394688
出版年份:
2015
页数:
284
语言:
English
文件大小:
3.33 MB
文件格式:
PDF

图书描述

Probabilistic graphical models is a technique in machine learning that uses the concepts of graph theory to concisely represent and optimally predict values in our data problems.

Graphical models gives us techniques to find complex patterns in the data and are widely used in the field of speech recognition, information extraction, image segmentation, and modeling gene regulatory networks.

This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also run different inference algorithms on them. There is an entire chapter that goes on to cover Naive Bayes model and Hidden Markov models. These models have been thoroughly discussed using real-world examples.

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