Computer Science

Deep Learning with R, Second Edition

Deep Learning with R, Second Edition

  • Downloads：4975
  • Type：Epub+TxT+PDF+Mobi
  • Create Date：2022-10-08 08:52:47
  • Update Date：2025-09-06
  • Status：finish
  • Author：François Chollet
  • ISBN：1633439844
  • Environment：PC/Android/iPhone/iPad/Kindle
  • Download

Summary

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

Download

Reviews

Andrés Hernández ，

It’s a great book for first contar with DL。 However， I think their approach of “no math， just code” is the weakest point of the book imo。 I get hey we’re going for a more approachable vibe， but I don’t think no maths at all is the way to go。

Mark ，

Even if not learning R the general chapters on deep learning are very good。 The R code seemed sub-optimal as in more like Python code literally ported across to R， not as R-native as I think it should be for an R focused book。

Nate ，

An excellent introduction to Deep Learning。 The author does a particularly good job putting Deep Learning into context （both social context and statistical context）， which is sometimes missing from computer science oriented books。 If you want to learn about Deep Learning and R is your language of choice this is the book for you。 （There's also a version using Python if that's your preference）。 An excellent introduction to Deep Learning。 The author does a particularly good job putting Deep Learning into context （both social context and statistical context）， which is sometimes missing from computer science oriented books。 If you want to learn about Deep Learning and R is your language of choice this is the book for you。 （There's also a version using Python if that's your preference）。 。。。more

Auggie Heschmeyer ，

While the difficulty of this books grows rapidly from chapter to chapter， Chollet somehow managed to keep things as simple and understandable as possible。 A lot of this is due to his （brilliant） decision to eschew mathematical notation on favor of R code。 Perhaps this is a detriment to the mathematically-minded， but as someone who learned stats through code， it made the topic so much more accessible。

Catherine Li ，

I am quite lost reading this book trying to understand how deep learning works。。 I guess the R sample code would be useful， but I learned so much better by taking the coursera class and especially did the homework in terms of understanding the algorithms。

David Wiley ，

Of the many books， articles， and videos I have watched and read on the topic of deep learning， this book stands head and shoulders above the rest。 Incredibly clear and straightforward explication。 I can't recommend it highly enough。 Of the many books， articles， and videos I have watched and read on the topic of deep learning， this book stands head and shoulders above the rest。 Incredibly clear and straightforward explication。 I can't recommend it highly enough。 。。。more

A Mig ，

Loved it， and I will definitively go back to it many many times in my Deep Learning journey with R programming。 The metaphor of the crumpled paper ball was perfect。

Andrew Breza ，

A clear introduction to an incredibly complex topic。 A must read for all data scientists who use R and want to stay on the cutting edge。

Andrew Nguyen ，

This book fills a nice niche in that it is the only reliable reference for using the R interface to Keras。 Of course， one of the authors is the founder of RStudio and main author of the Keras library。 The first three （and most important） chapters are free。 But come on。。。 just make your online documentation really good and save a book for the deep dive。 I'll largely be reviewing the free chapters。 The best part about this book is hands-down the examples。 The examples in chapter 3 cover the most This book fills a nice niche in that it is the only reliable reference for using the R interface to Keras。 Of course， one of the authors is the founder of RStudio and main author of the Keras library。 The first three （and most important） chapters are free。 But come on。。。 just make your online documentation really good and save a book for the deep dive。 I'll largely be reviewing the free chapters。 The best part about this book is hands-down the examples。 The examples in chapter 3 cover the most common cases: a binary classifier， a multiclass classifier and a regression problem。 These were easy enough to figure out and generalize to my problem。 Also of great use was the section on validation and overfitting。 The code samples were clear and well thought-out， and the writing was entertaining （for a textbook）。There was lots of stuff that I didn't like though。 This is a book from a programmers point of view。 The explanation of tensor calculus using nested for-loops left my eyes watering。 I would have liked to see a reference to an undergraduate explanation of tensor calculus （which I had to seek out myself）。 The organization of the first fout chapters was not smooth。 I found myself constantly flipping between two， three and four because a lot of the information is repeated， but not consistently。 For example， I was looking for an explanation of the activation functions and there are different tables and explanations in both chapters two and three。 I think this book is due for some serious editing。 This book has a lot of potential to be a good， authoritative source on using the Keras library。 But I often found myself jumping between random Coursera videos， textbook chapters and chapters within this book to get a good sense for what model I should be implementing。 。。。more

Alvaro Tejada Galindo ，

Pretty nice book by the creator of Keras。 Also my first approach to Deep Learning。 A must read book for anyone trying to learn Keras on R。 But。。。and this might be because my inexperience in Deep Learning。。。but。。。I feel like most of the examples showed just how to display accuracy percentages。。。but not actual predictions。。。and that happened as I wanted to reuse a convolutional network trained with my own to make image classification。。。all worked fine in theory。。。but could never used to make a pre Pretty nice book by the creator of Keras。 Also my first approach to Deep Learning。 A must read book for anyone trying to learn Keras on R。 But。。。and this might be because my inexperience in Deep Learning。。。but。。。I feel like most of the examples showed just how to display accuracy percentages。。。but not actual predictions。。。and that happened as I wanted to reuse a convolutional network trained with my own to make image classification。。。all worked fine in theory。。。but could never used to make a prediction。。。and I think an example like this should had been included in the book。。。anyway。。besides that。。。it's an awesome book。。。 。。。more

Terran M ，

Good book， but unless you have a substantial investment in R infrastructure that you can't afford to abandon， you should get the Python version of it instead。 The deep neural network community has clearly standardized on Python， not R， and it is simply the better choice for any new project in that area if you get to pick。Also， do not believe the author's facile claims that this is the only book you need。 This book explains almost nothing about how deep learning actually works， and is actually mo Good book， but unless you have a substantial investment in R infrastructure that you can't afford to abandon， you should get the Python version of it instead。 The deep neural network community has clearly standardized on Python， not R， and it is simply the better choice for any new project in that area if you get to pick。Also， do not believe the author's facile claims that this is the only book you need。 This book explains almost nothing about how deep learning actually works， and is actually more like a user manual for Keras。 Provided you actually want an instruction manual for Keras， it's an excellent book。 If you want to understand something about Deep Learning， go read the book by Goodfellow et al。 They make a nice set， in either order or alternating between the two。 。。。more

Yang Yang ，

The book is very good for R users to learn the state-of-the-art deep learning technology。 This book provides many practical suggestions and intuitive explanations。 It would be even better if these book can provide some exercises。

Reference

google book https://www.google.com/search?tbm=bks&q=Deep Learning with R, Second Edition

hathitrust digital library https://babel.hathitrust.org/cgi/ls?q1=Deep Learning with R, Second Edition&field1=ocr&a=srchls&ft=ft&lmt=ft

The British Library https://bll01.primo.exlibrisgroup.com/discovery/search?query=any,contains,Deep Learning with R, Second Edition&tab=LibraryCatalog&search_scope=Not_BL_Suppress&vid=44BL_INST:BLL01&lang=en&offset=0