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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron (2022, Trade Paperback)

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About this product

Product Identifiers

PublisherO'reilly Media, Incorporated

ISBN-101098125975

ISBN-139781098125974

eBay Product ID (ePID)19057257302

Product Key Features

Number of Pages861 Pages

LanguageEnglish

Publication NameHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems

Publication Year2022

SubjectIntelligence (Ai) & Semantics, Natural Language Processing, Neural Networks, Data Processing, Programming Languages / Python, Computer Vision & Pattern Recognition

TypeTextbook

Subject AreaComputers

AuthorAurélien Géron

FormatTrade Paperback

Dimensions

Item Height1.8 in

Item Weight51 Oz

Item Length9.1 in

Item Width7.6 in

Additional Product Features

Edition Number3

Intended AudienceScholarly & Professional

LCCN2023-549175

Dewey Edition23

IllustratedYes

Dewey Decimal006.3/1

SynopsisThrough a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

LC Classification NumberQ325.5.G47 2022