Deep learning with R:
Introduces deep learning systems using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Shelter Island, NY
Manning
[2018]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Introduces deep learning systems using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xxi, 335 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9781617295546 161729554X |
Internformat
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Datensatz im Suchindex
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| adam_text | Fart 1
Part 2
brief contents
Fundamentals of deep learning...............................1
1 “ What is deep learning? 3
2 n Before we begin: the mathematical building blocks
of neural networks 24
3 * Getting started with neural networks 50
4 n Fundamentals of machine learning 84
Deep learning in practice.................................109
5 “ Deep learning for computer vision 111
6 ° Deep learning for text and sequences 164
7 n Advanced deep-learning best practices 218
8 “ Generative deep learning 250
9 a Conclusions 293
contents
preface xiii
acknowledgments xv
about this book xvi
about the authors xx
about the cover xxi
Part 1 Fundamentals of deep learning....... 1
i
What is deep learning? 3
1.1 Artificial intelligence, machine learning,
and deep learning 4
Artificial intelligence 4 ■ Machine learning 4 ■ Learning
representations from data 6 ■ The “deep in deep learning 8
Understanding how deep learning works, in three figures 9
What deep learning has achieved so far 11 ■ Don’t believe the
short-term hype 12 ■ The promise of AI 12
1.2 Before deep learning: a brief history
of machine learning 13
Probabilistic modeling 14 ■ Early neural networks 14
Kernel methods 15 • Decision trees, random forests, and gradient
boosting machines 16 ■ Back to neural networks 17 9 What
makes deep learning different 17 9 The modem machine-learning
landscape 18
Ml
CONTENTS
1.3 Why deep learning? Why now? 19
Hardware 19 ■ Data 20 ■ Algorithms 21 ■ A new wave of
investment 21 ■ The democratization of deep learning 22
Will it last ? 22
Before we begin: the mathematical building
blocks of neural networks 24
2.1 A first look at a neural network 25
2.2 Data representations for neural networks 29
Scalars (OD tensors) 29 ■ Vectors (ID tensors) 29 ■ Matrices
(2D tensors) 30 ■ 3D tensors and higher-dimensional tensors 30
Key attributes 30 ■ Manipulating tensors in R 31 ■ The notion
of data batches 32 ■ Real-world examples of data tensors 32
Vector data 32 ■ Timeseries data or sequence data 33
Image data 33 ■ Video data 34
2.3 The gears of neural networks: tensor operations 34
Element-wise operations 35 ■ Operations involving tensors of
different dimensions 36 ■ Tensor dot 36 ■ Tensor
reshaping 38 ■ Geometric interpretation of tensor operations 39
A geometric interpretation of deep learning 40
2.4 The engine of neural networks:
gradient-based optimization 41
WhaVs a derivative ? 42 ■ Derivative of a tensor operation: the
gradient 43 ■ Stochastic gradient descent 44 ■ Chaining
derivatives: the Backpropagation algorithm 46
2.5 Looking back at our first example 47
2.6 Summary 49
Getting started with neural networks 50
3.1 Anatomy of a neural network 51
Layers: the building blocks of deep learning 52 ■ Models: networks
of layers 52 ■ Loss functions and optimizers: keys to configuring
the learning process 53
3.2 Introduction to Keras 54
Keras, TensorFlow, Theano, and CNTK 54 ■ Installing
Keras 56 ■ Developing with Keras: a quick overview 56
3.3 Setting up a deep-learning workstation 57
Getting Keras running: two options 58 ■ Running deep-learning
jobs in the cloud: pros and cons 58 ■ What is the best GPU for deep
learning? 59
CONTENTS
IX
3.4 Classifying movie reviews:
a binary classification example 59
The IMDB dataset 59 • Preparing the data 61 ■ Building
your network 62 ■ Validating your approach 65 ■ Using a
trained network to generate predictions on new data 68 ■ Further
experiments 69 ■ Wrapping up 69
3.5 Classifying newswires: a multiclass classification
example 70
The Reuters dataset 70 ■ Preparing the data 71
Building your network 72 ■ Validating your approach 73 *
Generating predictions on new data 74 ■ A different way to handle
the labels and the loss 75 ■ The importance of having sufficiently
large intermediate layers 75 ■ Further
experiments 76 ■ Wrapping up 76
3.6 Predicting house prices: a regression example 76
The Boston Housing Price dataset 77 ■ Preparing the data 77
Building your network 78 ■ Validating your approach using
K-fold validation 79 ■ Wrapping up 83
3.7 Summary 83
Fundamentals of machine learning 84
4.1 Four branches of machine learning 85
Supervised learning 85 ■ Unsupervised learning 85
Self-supervised learning 86 • Reinforcement learning 86
4.2 Evaluating machine-learning models 87
Training, validation, and test sets 88 ■ Things to keep
in mind 91
4.3 Data preprocessing, feature engineering,
and feature learning 91
Data preprocessing for neural networks 91 ■ Feature
engineering 93
4.4 Overfitting and underfitting 94
Reducing the network s size 95 ■ Adding weight
regularization 98 ■ Adding dropout 100
4.5 The universal workflow of machine learning 102
Defining the problem and assembling a dataset 102 • Choosing
a measure of success 103 ■ Deciding on an evaluation
protocol 104 ■ Preparing your data 104
X
CONTENTS
Developing a model that does better than a baseline 104
Scaling up: developing a model that overfits 105
Regularizing your model and tuning your hyperparameters 106
4.6 Summary 107
Part 2
Deep learning in practice
I
Deep learning for computer vision 111
5.1 Introduction to convnets 111
The convolution operation 114 ■ The max-pooling
operation 119
5.2 Training a convnet from scratch on a small dataset 121
The relevance of deep learning for small-data problems 121
Downloading the data 122 ■ Building your network 124
Data preprocessing 126 ■ Using data augmentation 128
5.3 Using a pretrained convnet 132
Feature extraction 133 ■ Fine-tuning 142
Wrapping up 146
5.4 Visualizing what convnets learn 146
Visualizing intermediate activations 146 ■ Visualizing convnet
filters 153 ■ Visualizing heatmaps of class activation 159
5.5 Summary 163
Deep learning for text and sequences 164
6.1 Working with text data 165
One-hot encoding of words and characters 166 ■ Using word
embeddings 169 ■ Putting it all together: from raw text to word
embeddings 174 ■ Wrapping up 180
6.2 Understanding recurrent neural networks 180
A recurrent layer in Kerns 182 ■ Understanding the LSTM and
GRU layers 186 * A concrete LSTM example in Keras 188
Wrapping up 190
6.3 Advanced use of recurrent neural networks 190
A temperature-forecasting problem 191 ■ Preparing the data 193
A common-sense, non-machine-leaming baseline 197 ■ A basic
machine-learning approach 198 ■ A first recurrent baseline 199
Using recurrent dropout to fight overfitting 201 ■ Stacking
recurrent layers 202 ■ Using bidirectional RNNs 204
Going even further 207 * Wrapping up 208
CONTENTS
XI
6.4 Sequence processing with convnets 209
Understanding ID convolution for sequence data 209
ID pooling for sequence data 210 ■ Implementing a ID
convnet 210 ■ Combining CNNs and RNNs to process long
sequences 212 ■ Wrapping up 216
6.5 Summary 216
/ Advanced deep-learning best practices 218
7.1 Going beyond the sequential model:
the Keras functional API 219
Introduction to the functional API 221 ■ Multi-input
models 222 ■ Multi-output models 224 ■ Directed acyclic
graphs of layers 221 ■ Layer weight sharing 231 ■ Models as
layers 232 ■ Wrapping up 233
7.2 Inspecting and monitoring deep-learning models using
Keras callbacks and TensorBoard 233
Using callbacks to act on a model during training 233
Introduction to TensorBoard: the TensorFlow visualization
framework 236 ■ Wrapping up 241
7.3 Getting the most out of your models 241
Advanced architecture patterns 241 ■ Hyperparameter
optimization 245 ■ Model ensembling 246 ■ Wrapping
up 248
7.4 Summary 249
Generative deep learning 250
8.1 Text generation with LSTM 252
A brief history of generative recurrent networks 252
How do you generate sequence data ? 253 • The importance
of the sampling strategy 253 • Implementing character-level
LSTM text generation 255 ■ Wrapping up 260
8.2 DeepDream 260
Implementing DeepDream in Keras 261 ■ Wrapping up 261
8.3 Neural style transfer 267
The content loss 268 ■ The style loss 268 ■ Neural style transfer
in Keras 269 ■ Wrapping up 214
8.4 Generating images with variational autoencoders 276
Sampling from latent spaces of images 216 * Concept vectors for
image editing 211 ■ Variational autoencoders 218
Wrapping up 284
xii
CONTENTS
8.5 Introduction to generative adversarial networks 284
A schematic GAN implementation 286 ■ A bag of tricks 286
The generator 287 • The discriminator 288
The adversarial network 289 ■ How to train your
DCGAN 290 * Wrapping up 292
8.6 Summary 292
9
Conclusions 293
9.1 Key concepts in review 294
Various approaches to AI 294 ■ What makes deep learning special
within the field of machine learning 294 ■ How to think about
deep learning 295 ■ Key enabling technologies 296
The universal machine-learning workflow 297 ■ Key netivork
architectures 298 ■ The space of possibilities 302
9.2 The limitations of deep learning 303
The risk of anthropomorphizing machine-learning models 304
Local generalization vs. extreme generalization 306
Wrapping up 307
9.3 The future of deep learning 307
Models as programs 308 ■ Beyond backpropagation and
differentiable layers 310 ■ Automated machine learning 310
Lifelong learning and modular subroutine reuse 311 • The long-
term vision 313
9.4 Staying up to date in a fast-moving field 313
Practice on real-world problems using Kaggle 314 ■ Read about
the latest developments on arXiv 314 ■ Explore the Keras
ecosystem 315
9.5 Final words 315
appendix A Installing Keras and its dependencies on Ubuntu 316
appendix B Running RStudio Server on an EC2 GPU instance 320
index 327
|
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| author | Chollet, François Allaire, J. J. 1969- |
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| format | Book |
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| id | DE-604.BV044743224 |
| illustrated | Illustrated |
| indexdate | 2024-08-01T10:43:02Z |
| institution | BVB |
| isbn | 9781617295546 161729554X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030139017 |
| oclc_num | 1027009658 |
| open_access_boolean | |
| owner | DE-20 DE-83 DE-945 DE-739 DE-863 DE-BY-FWS DE-355 DE-BY-UBR DE-11 DE-384 DE-19 DE-BY-UBM DE-703 DE-521 |
| owner_facet | DE-20 DE-83 DE-945 DE-739 DE-863 DE-BY-FWS DE-355 DE-BY-UBR DE-11 DE-384 DE-19 DE-BY-UBM DE-703 DE-521 |
| physical | xxi, 335 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Manning |
| record_format | marc |
| spellingShingle | Chollet, François Allaire, J. J. 1969- Deep learning with R Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Keras Framework, Informatik (DE-588)1160521077 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd Text Mining (DE-588)4728093-1 gnd Deep learning (DE-588)1135597375 gnd R Programm (DE-588)4705956-4 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4129594-8 (DE-588)1160521077 (DE-588)4193754-5 (DE-588)4123037-1 (DE-588)4728093-1 (DE-588)1135597375 (DE-588)4705956-4 (DE-588)4033447-8 |
| title | Deep learning with R |
| title_auth | Deep learning with R |
| title_exact_search | Deep learning with R |
| title_full | Deep learning with R François Chollet, with J.J. Allaire |
| title_fullStr | Deep learning with R François Chollet, with J.J. Allaire |
| title_full_unstemmed | Deep learning with R François Chollet, with J.J. Allaire |
| title_short | Deep learning with R |
| title_sort | deep learning with r |
| topic | Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Keras Framework, Informatik (DE-588)1160521077 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd Text Mining (DE-588)4728093-1 gnd Deep learning (DE-588)1135597375 gnd R Programm (DE-588)4705956-4 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Neuronales Netz Maschinelles Sehen Keras Framework, Informatik Maschinelles Lernen Datenanalyse Text Mining Deep learning R Programm Künstliche Intelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030139017&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cholletfrancois deeplearningwithr AT allairejj deeplearningwithr |
Inhaltsverzeichnis
Würzburg Zentralbibliothek Lesesaal
| Signatur: |
1000 ST 302 R01 C5 |
|---|---|
| Exemplar 1 | ausleihbar Verfügbar Bestellen |