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Hands-On Machine Learning with - Paperback, by Géron Aurélien - Very Good
USD35,00
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Ubicado en: Everett, Washington, Estados Unidos
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Entrega prevista entre el sáb. 28 sep. y el jue. 3 oct. a 43230
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N.º de artículo de eBay:266608302051
Características del artículo
- Estado
- Book Title
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlo
- ISBN
- 9781492032649
- Subject Area
- Mathematics, Computers
- Publication Name
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
- Publisher
- O'reilly Media, Incorporated
- Item Length
- 9.4 in
- Subject
- Intelligence (Ai) & Semantics, General, Data Processing, Computer Vision & Pattern Recognition
- Publication Year
- 2019
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 1.4 in
- Item Weight
- 43.2 Oz
- Item Width
- 7 in
- Number of Pages
- 856 Pages
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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
856 Pages
Language
English
Publication Name
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
Subject
Intelligence (Ai) & Semantics, General, Data Processing, Computer Vision & Pattern Recognition
Publication Year
2019
Type
Textbook
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
2020-304725
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 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., 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
LC Classification Number
QA76.73.P98G45 2019
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