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“Spine is Tight has NO creases! Pages and cover are clean and intact. Shows little signs of shelf ...
Brand
Unbranded
MPN
Does not apply
ISBN
9781492032649
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Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1492032646
ISBN-13
9781492032649
eBay Product ID (ePID)
8038668355

Product Key Features

Number of Pages
848 Pages
Language
Eng,Swa
Publication Name
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
Publication Year
2019
Subject
Intelligence (Ai) & Semantics, General, Data Processing, Computer Vision & Pattern Recognition
Type
Textbook
Author
Aurélien Géron
Subject Area
Mathematics, Computers
Format
Trade Paperback

Dimensions

Item Height
1.4 in
Item Weight
43.2 Oz
Item Length
9.4 in
Item Width
7 in

Additional Product Features

Edition Number
2
Intended Audience
Trade
LCCN
2024-331465
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Synopsis
Through a series of recent 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 practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks'??Scikit-Learn and TensorFlow'??author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'??ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you'??ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets, Through a series of recent 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 practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author Aur lien G ron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets, Now fully updated, this bestselling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help users gain an intuitive understanding of the concepts and tools for building intelligent systems.t systems.
LC Classification Number
QA76.73.P98G45 2019

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