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Название : Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python
Автор : Ankur Ankan, Abinash Panda
Издательство: Packt Publishing
Год издания : 2015
Страниц: 284
Формат : PDF
Размер файла: 16 MB
Язык : English
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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По кнопке выше можно купить бумажные варианты этой книги и похожих книг на сайте интернет-магазина "Лабиринт".
Using the button above you can buy paper versions of this book and similar books on the website of the "Labyrinth" online store.
Реклама. ООО "ЛАБИРИНТ.РУ", ИНН: 7728644571, erid: LatgCADz8.
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