Mastering Probabilistic Graphical Models Using Python: Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python
Book information
Description
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.
Similar books
Problem Solving with Algorithms and Data Structures Using Python. Release 3.0
Functional Programming in Python
EPUB
Functional Programming in Python
MOBI
Mastering Python for Data Science
Написание скриптов для Blender 2.49
Learning Data Mining with Python: Harness the power of Python to analyze data and create insightful predictive models
Python и анализ данных (+ исходные коды и дополнительные материалы с сайта поддержки)
RAR