Book

Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems

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Through 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 Aur?lien G?ron 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

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Availability

Detail Information

Series Title

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Call Number

006.31 GER

Publisher

Beijing : O\'Reilly Media, Beijing ; Boston., 2022

Collation

xx, 551 pages : illustrations ; 24 cm

Language

English

ISBN/ISSN

9781491-96-2299

Classification

006.31

Content Type

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Media Type

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Carrier Type

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Edition

2nd Edition

Subject(s)

Computer Science Software Engineering
1. Computer Science 2. Software Engineering 3. Dat
Data Science Information Technology

Specific Detail Info

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Statement of Responsibility

Ge?ron, Aure?lien

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