Hands-On Machine Learning with Scikit-Learn and TensorFlow
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Lieferung aus: Vereinigte Staaten von Amerika, In Stock, plus shipping.
Business, Data Analysis & Visualization, 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. 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. ebook.

Lieferung aus: Vereinigte Staaten von Amerika, in-stock.
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 netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details.

Lieferung aus: Vereinigte Staaten von Amerika, ebook for download, Free shipping.
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 netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details, Kindle Edition, Edition: 1, Format: Kindle eBook, Label: O'Reilly Media, O'Reilly Media, Product group: eBooks, Published: 2017-03-13, Release date: 2017-03-13, Studio: O'Reilly Media, Sales rank: 19411.

Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, ebook for download, Free shipping.
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 netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details, Kindle Edition, Edition: 1, Format: Kindle eBook, Label: O'Reilly Media, O'Reilly Media, Product group: eBooks, Published: 2017-03-13, Release date: 2017-03-13, Studio: O'Reilly Media, Sales rank: 43612.

Lieferung aus: Deutschland, E-Book zum Download, Versandkostenfrei.
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 netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details, Kindle Ausgabe, Ausgabe: 1, Format: Kindle eBook, Label: O'Reilly Media, O'Reilly Media, Produktgruppe: eBooks, Publiziert: 2017-03-13, Freigegeben: 2017-03-13, Studio: O'Reilly Media, Verkaufsrang: 14769.

Lieferung aus: Kanada, E-Book zum Download, Versandkostenfrei.
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 netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details, Kindle Edition, Ausgabe: 1, Format: Kindle eBook, Label: O'Reilly Media, O'Reilly Media, Produktgruppe: eBooks, Publiziert: 2017-03-13, Freigegeben: 2017-03-13, Studio: O'Reilly Media, Verkaufsrang: 11891.

Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, ספר אלקטרוני להורדה, משלוח חינם.
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 netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details, Kindle Edition, מהדורה: 1, תבנית: Kindle eBook, תווית: O'Reilly Media, O'Reilly Media, קבוצת המוצרים: eBooks, לאור: 2017-03-13, תאריך שחרור: 2017-03-13, סטודיו: O'Reilly Media, דרגה מכירות: 18980.

Lieferung aus: Vereinigte Staaten von Amerika, In Stock.
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