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Apprentissage automatique pratique avec Scikit - Apprendre et TensorFlow - Géron, Aurélien
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Livraison :
6,72 USD (environ 5,75 EUR) USPS Media MailTM.
Lieu où se trouve l'objet : Fairfield, Connecticut, États-Unis
Délai de livraison :
Estimé entre le jeu. 24 juil. et le lun. 28 juil.
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Caractéristiques de l'objet
- État
- Book Title
- Hands–On Machine Learning with Scikit–Learn and TensorFlow
- Genre
- Machine learning
- ISBN
- 9781491962299
À propos de ce produit
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1491962291
ISBN-13
9781491962299
eBay Product ID (ePID)
227662629
Product Key Features
Number of Pages
572 Pages
Publication Name
Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
Language
English
Publication Year
2017
Subject
Intelligence (Ai) & Semantics, Data Processing, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
1.1 in
Item Weight
34.8 Oz
Item Length
9.2 in
Item Width
7.1 in
Additional Product Features
Intended Audience
Trade
LCCN
2018-418542
Illustrated
Yes
Synopsis
Graphics in this book are printed in black and white . 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 Apply practical code examples without acquiring excessive machine learning theory or algorithm details, Graphics in this book are printed in black and white . 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 Apply practical code examples without acquiring excessive machine learning theory or algorithm details
LC Classification Number
Q325.5
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