Deep Learning with R (Part 1)
Seiten
2018
Manning Publications (Verlag)
978-1-61729-554-6 (ISBN)
Manning Publications (Verlag)
978-1-61729-554-6 (ISBN)
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Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind photo tagging, self-driving cars, virtual assistants and other previously impossible applications.
Deep Learning with R is for developers and data scientists with some R experience who want to use deep learning to solve real-world problems. The book is structured around a series of practical examples that introduce each new concept and demonstrate best practices. You'll begin by learning what deep learning is, how it connects with AI and Machine Learning, and why it's rapidly gaining in importance right now. You'll then dive into practical applications of computer vision, natural language processing, and more.
Key features
* Understand key machine learning concepts
* Set up a computer environment for deep learning
* Visualize neural networks
* Use recurrent neural networks for text and sequence Classification
Audience
You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is required.
About the technology
Although deep learning can be a challenging subject, new technologies make it much easier to get started than ever before. The Keras deep learning library featured in this book puts ease of use and accessibility front and center, making it a great fit for new practitioners.
Deep Learning with R is for developers and data scientists with some R experience who want to use deep learning to solve real-world problems. The book is structured around a series of practical examples that introduce each new concept and demonstrate best practices. You'll begin by learning what deep learning is, how it connects with AI and Machine Learning, and why it's rapidly gaining in importance right now. You'll then dive into practical applications of computer vision, natural language processing, and more.
Key features
* Understand key machine learning concepts
* Set up a computer environment for deep learning
* Visualize neural networks
* Use recurrent neural networks for text and sequence Classification
Audience
You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is required.
About the technology
Although deep learning can be a challenging subject, new technologies make it much easier to get started than ever before. The Keras deep learning library featured in this book puts ease of use and accessibility front and center, making it a great fit for new practitioners.
Francois Chollet is a deep learning researcher at Google and the author of the Keras deep learning library. He blogs about deep learning at blog.keras.io.
J.J. Allaire is the Founder of RStudio and the creator of the RStudio IDE. J.J. is the author of the R interfaces to TensorFlow and Keras.
PART 1 - FUNDAMENTALS OF DEEP LEARNING
What is deep learning?
Before we begin: the mathematical building blocks of neural networks
Getting started with neural networks
Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
Deep learning for computer vision
Deep learning for text and sequences
Advanced deep-learning best practices
Generative deep learning
Conclusions
| Erscheinungsdatum | 14.06.2018 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 187 x 235 mm |
| Gewicht | 603 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-61729-554-X / 161729554X |
| ISBN-13 | 978-1-61729-554-6 / 9781617295546 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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