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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
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