Deep learning with Python:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Shelter Island, NY
Manning
[2018]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxi, 361 Seiten Illustrationen, Diagramme |
| ISBN: | 9781617294433 1617294438 |
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Part 1
Part 2
brief contents
Fundamentals of deep learning.1
1 ■ What is deep learning? 3
2 ■ Before we begin: the mathematical building blocks of neural
networks 25
3 ■ Getting started with neural networks 56
4 ■ Fundamentals of machine learning 93
Deep learning in practice.117
5 ■ Deep learning for computer vision 119
6 ■ Deep learning for text and sequences 178
7 ■ Advanced deep-learning best practices 233
8 ■ Generative deep learning 269
9 ■ Conclusions 314
contents
preface xiii
acknowledgments xv
about this book xvi
about the author xx
about the cover xxi
Fart 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 13
1.2 Before deep learning: a brief history of machine
learning 14
Probabilistic modeling 14 ■ Early neural networks 14
Kernel methods 15 ■ Decision trees, random forests,
and gradient boosting machines 16 ■ Back to neural
networks 17 ■ What makes deep learning different 17
The modem machine-learning landscape 18
vu
CONTENTS
«M
vin
1.3 Why deep learning? Why now? 20
Hardware 20 ■ Data 21 ■ Algorithms 21 ■ A new
wave of investment 22 ■ The democratization of deep
learning 23 ■ Will it last'? 23
Before we begin: the mathematical building blocks of
neural networks 25
2.1 A first look at a neural network 27
2.2 Data representations for neural networks 31
Scalars (0D tensors) 31 ■ Vectors (ID tensors) 31
Matrices (2D tensors) 31 ■ 3D tensors and higher-
dimensional tensors 32 • Key attributes 32
Manipulating tensors in Numpy 34 ■ The notion
of data batches 34 ■ Real-world examples of data
tensors 35 ■ Vector data 35 ■ Timeseries data or
sequence data 35 ■ Image data 36 ■ Video data 31
2.3 The gears of neural networks: tensor operations 38
Element-wise operations 38 ■ Broadcasting 39 ■ Tensor
dot 40 ■ Tensor reshaping 42 ■ Geometric interpretation
of tensor operations 43 ■ A geometric interpretation of deep
learning 44
2.4 The engine of neural networks: gradient-based
optimization 46
What’s a derivative? 47 ■ Derivative of a tensor operation:
the gradient 48 ■ Stochastic gradient descent 48
Chaining derivatives: the Backpropagation algorithm 51
2.5 Looking back at our first example 53
2.6 Chapter summary 55
Getting started with neural networks 56
3.1 Anatomy of a neural network 58
Layers: the building blocks of deep learning 58 ■ Models:
networks of layers 59 ■ Loss functions and optimizers: keys
to configuring the learning process 60
3.2 Introduction to Keras 61
Keras, TensorFlow, Theano, and CNTK 62 ■ Developing
with Keras: a quick overview 62
3.3 Setting up a deep-learning workstation 65
Jupyter notebooks: the preferred way to run deep-learning
experiments 65 ■ Getting Keras running: two options 66
CONTENTS
ix
Running deep-learning jobs in the cloud: pros and cons 66
What is the best GPU for deep learning? 66
3.4 Classifying movie reviews: a binary classification
example 68
The IMDB dataset 68 ■ Preparing the data 69
Building your network 70 • Validating your approach 73
Using a trained network to generate predictions on new
data 76 ■ Further experiments 77 ■ Wrapping up 77
3.5 Classifying newswires: a multiclass classification
example 78
The Reuters dataset 78 ■ Preparing the data 79
Building your network 79 ■ Validating your approach 80
Generating predictions on new data 83 ■ A different way to
handle the labels and the loss 83 ■ The importance of
having sufficiently large intermediate layers 83 ■ Further
experiments 84 ■ Wrapping up 84
3.6 Predicting house prices: a regression example 85
The Boston Housing Price dataset 85 ■ Preparing the
data 86 ■ Building your network 86 ■ Validating
your approach using K-fold validation 87 • Wrapping up 91
3.7 Chapter summary 92
Fundamentals of machine learning 93
4.1 Four branches of machine learning
94
Supervised learning 94 ■ Unsupervised learning 94
Self-supervised learning 94 ■ Reinforcement learning 95
4.2 Evaluating machine-learning models 97
Training; validation, and test sets 97 ■ Things to
keep in mind 100
4.3 Data preprocessing, feature engineering,
and feature learning 101
Data preprocessing for neural networks 101 ■ Feature
engineering 102
4.4 Overfitting and underfitting 104
Reducing the network's size 104 ■ Adding weight
regularization 107 * Adding dropout 109
4.5 The universal workflow of machine learning 111
Defining the problem and assembling a dataset 111
Choosing a measure of success 112 ■ Deciding on an
X
CONTENTS
evaluation protocol 112 ■ Preparing your data 112
Developing a model that does better than a baseline 113
Scaling up: developing a model that overfits 114
Regularizing your model and tuning your hyperparameters 114
4.6 Chapter summary 116
Part 2 Deep learning in practice.117
Deep learning for computer vision 119
5.1 Introduction to convnets 120
The convolution operation 122 ■ The max-pooling
operation 127
5.2 Training a convnet from scratch on a small dataset 130
The relevance of deep learning for small-data problems 130
Downloading the data 131 ■ Building your network 133
Data preprocessing 135 ■ Using data augmentation 138
5.3 Using a pretrained convnet 143
Feature extraction 143 ■ Fine-tuning 152 ■ Wrapping
up 159
5.4 Visualizing what convnets learn 160
Visualizing intermediate activations 160 ■ Visualizing
convnet filters 167■ Visualizing heatmaps of class
activation 172
5.5 Chapter summary 177
Deep learning for text and sequences
6.1 Working with text data 180
178
One-hot encoding of xvords and characters 181 ■ Using
word embeddings 184 ■ Putting it all together: from raw
text to word embeddings 188 ■ Wrapping up 195
6.2 Understanding recurrent neural networks 196
A recurrent layer in Keras 198 ■ Understanding the
LSTM and GRU layers 202 ■ A concrete LSTM example
in Keras 204 ■ Wrapping up 206
6.3 Advanced use of recurrent neural networks 207
A temperature-forecasting problem 207 ■ Preparing the
data 210 1 A common-sense, non-machine-leaming
baseline 212 ■ A basic machine-learning approach 213
A first recurrent baseline 215 ■ Using recurrent dropout
CONTENTS
xi
to fight overfitting 216 ■ Stacking recurrent layers 217
Using bidirectional RNNs 219 ■ Going even further 222
Wrapping up 223
6.4 Sequence processing with convnets 225
Understanding ID convolution for sequence data 225
ID pooling for sequence data 226 ■ Implementing a ID
convnet 226 ■ Combining CNNs and RNNs to process long
sequences 228 ■ Wrapping up 231
6.5 Chapter summary 232
J Advanced deep-learning best practices
7.1 Going beyond the Sequential model: the Keras
functional API 234
Introduction to the functional API 236 ■ Multi-input
models 238 ■ Multi-output models 240 ■ Directed acyclic
graphs of layers 242 ■ Layer weight sharing 246 ■ Models
as layers 247 * Wrapping up 248
7.2 Inspecting and monitoring deep-learning models using
Keras callbacks and TensorBoard 249
Using callbacks to act on a model during training 249
Introduction to TensorBoard: the TensorFlow visualization
framework 252 ■ Wrapping up 259
7.3 Getting the most out of your models 260
Advanced architecture patterns 260 * Hyperparameter
optimization 263 ■ Model ensembling 264 * Wrapping
up 266
7.4 Chapter summary 268
Generative deep learning 269
8.1 Text generation with LSTM 271
A brief history of generative recurrent networks 271 ■ How
do you generate sequence data ? 272 ■ The importance of
the sampling strategy 272 * Implementing character-level
LSTM text generation 274 ■ Wrapping up 279
8.2 DeepDream 280
Implementing DeepDream in Keras 281 ■ Wrapping up 286
8.3 Neural style transfer 287
The content loss 288 ■ The style loss 288 ■ Neural style
transfer in Keras 289 • Wrapping up 295
Xll
CONTENTS
8.4 Generating images with variational autoencoders 296
Sampling from latent spaces of images 296 9 Concept vectors
for image editing 297 ■ Variational autoencoders 298
Wrapping up 304
8.5 Introduction to generative adversarial networks 305
A schematic GAN implementation 307 ■ A bag of tricks 307
The generator 308 9 The discriminator 309 ■ The adversarial
network 310 9 How to train your DCGAN 310 9 Wrapping
up 312
8.6 Chapter summary 313
Conclusions 314
9.1 Key concepts in review 315
Various approaches to AI 315 ■ What makes deep learning
special within the field of machine learning 315 ■ How to
think about deep learning 316 ■ Key enabling technologies 317
The universal machine-learning workflow 318 ■ Key network
architectures 319 ■ The space of possibilities 322
9.2 The limitations of deep learning 325
The risk of anthropomorphizing machine-learning models 325
Local generalization vs. extreme generalization 32 7
Wrapping up 329
9.3 The future of deep learning 330
Models as programs 330 ■ Beyond backpropagation and
differentiable layers 332 ■ Automated machine learning 332
Lifelong learning and modular subroutine reuse 333
The long-term vision 335
9.4 Staying up to date in a fast-moving field 337
Practice on real-world problems using Kaggle 33 7
Read about the latest developments on arXiv 337
Explore the Kerns ecosystem 338
9.5 Final words 339
appendix A Installing Keras and its dependencies on Ubuntu 340
appendix B RunningJupy ter notebooks on an EC2 GPU instance 345
index 353 |
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| language | English |
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| title | Deep learning with Python |
| title_auth | Deep learning with Python |
| title_exact_search | Deep learning with Python |
| title_full | Deep learning with Python François Chollet |
| title_fullStr | Deep learning with Python François Chollet |
| title_full_unstemmed | Deep learning with Python François Chollet |
| title_short | Deep learning with Python |
| title_sort | deep learning with python |
| topic | Keras Framework, Informatik (DE-588)1160521077 gnd Deep Learning (DE-588)1135597375 gnd Python Programmiersprache (DE-588)4434275-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Keras Framework, Informatik Deep Learning Python Programmiersprache Maschinelles Lernen |
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