Deep Learning with R, Second Edition
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Sprache:Englisch
69,99 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
26.07.2022
Verlag
Pearson AcademicSeitenzahl
568
Maße (L/B/H)
23,2/18,6/3,1 cm
Gewicht
856 g
Auflage
2nd edition
Sprache
Englisch
ISBN
978-1-63343-984-9
Deep learning from the ground up using R and the powerful Keras library!
In Deep Learning with R, Second Edition you will learn:
Deep learning from first principles
Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.
About the book
Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library.
What's inside
Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
About the reader
For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.
About the author
François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book.
Table of Contents
1 What is deep learning?
2 The mathematical building blocks of neural networks
3 Introduction to Keras and TensorFlow
4 Getting started with neural networks: Classification and regression
5 Fundamentals of machine learning
6 The universal workflow of machine learning
7 Working with Keras: A deep dive
8 Introduction to deep learning for computer vision
9 Advanced deep learning for computer vision
10 Deep learning for time series
11 Deep learning for text
12 Generative deep learning
13 Best practices for the real world
14 Conclusions
In Deep Learning with R, Second Edition you will learn:
Deep learning from first principles
Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.
About the book
Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library.
What's inside
Image classification and image segmentation
Time series forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
About the reader
For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.
About the author
François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book.
Table of Contents
1 What is deep learning?
2 The mathematical building blocks of neural networks
3 Introduction to Keras and TensorFlow
4 Getting started with neural networks: Classification and regression
5 Fundamentals of machine learning
6 The universal workflow of machine learning
7 Working with Keras: A deep dive
8 Introduction to deep learning for computer vision
9 Advanced deep learning for computer vision
10 Deep learning for time series
11 Deep learning for text
12 Generative deep learning
13 Best practices for the real world
14 Conclusions
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