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Overview
In Deep Learning with Python, Second Edition you will learn:
Deep learning from first principles
Image classification and image segmentation
Timeseries forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Full color printing throughout
Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach—even if you have no background in mathematics or data science. This book shows you how to get started.
About the book
Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications.
What's inside
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
Full color printing throughout
About the reader
For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.
About the author
François Chollet is a software engineer at Google and creator of the Keras deep-learning library.
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 timeseries
11 Deep learning for text
12 Generative deep learning
13 Best practices for the real world
14 Conclusions
Product Details
| ISBN-13: | 9781617296864 |
|---|---|
| Publisher: | Manning |
| Publication date: | 12/21/2021 |
| Pages: | 504 |
| Sales rank: | 420,834 |
| Product dimensions: | 7.38(w) x 9.25(h) x 1.30(d) |
About the Author
Table of Contents
Preface xvii
Acknowledgments xix
About this book xx
About the author xxiii
About the cover illustration xxiv
1 What is deep learning? 1
1.1 Artificial intelligence, machine learning, and deep learning 2
Artificial intelligence 2
Machine homing 3
Learning rules and representations from data 4
The "deep" in "deep learning" 7
Understanding how deep learning works, in three figures 8
What deep learning has achieved so far 10
Don't believe the short-term hype 11
The promise of AI 12
1.2 Before deep learning: A brief history of machine learning 13
Probabilistic modeling 13
Early neural networks 14
Kernel methods 14
Decision trees, random forests, and gradient boosting machines 15
Back to neural networks 16
What makes deep learning different 17
The modern machine learning landscape 18
1.3 Why deep learning? Why now? 20
Hardware 20
Data 21
Algorithms 22
A new wave of investment 23
The democratization of deep learning 24
Will it last? 24
2 The mathematical building blacks of neural networks 26
2.1 A first look at a neural network 27
2.2 Data representations for neural networks 31
Scalars (rank-0 tensors) 31
Vectors (rank-1 tensors) 31
Matrices (rank-2 tensors) 32
Rank-3 and higher-rank tensors 32
Key attributes 32
Manipulating tensors in NumPy 34
The notion of data batches 35
Real-world examples of data tensors 35
Vector data 35
Timeseries data or sequence data 36
Image data 37
Video data 37
2.3 The gears of neural networks: Tensor operations 38
Element-wise operations 38
Broadcasting 40
Tensor product 41
Tensor reshaping 43
Geometric interpretation of tensor operations 44
A geometric interpretation of deep learning 47
2.4 The engine of neural networks: Gradient-based optimization 48
What's a derivative? 49
Derivative of a tensor operation: The gradient 51
Stochastic gradient descent 52
Chaining derivatives: The Backpropagation algorithm 55
2.5 Looking back at our first example 61
Reimplementing our first example from scratch in TensorFlow 63
Running one training step 64
The full training loop 65
Evaluating the model 66
3 Introduction to Keras and TensorFlow 68
3.1 What's TensorFlow? 69
3.2 What's Keras? 69
3.3 Keras and TensorFlow: A brief history 71
3.4 Setting up a deep learning workspace 71
Jupyter notebooks: The preferred way to ran deep learning experiments 72
Using Colaboratory 73
3.5 First steps with TensorFlow 75
Constant tensors and variables 76
Tensor operations: Doing math in TensorFlow 78
A second look at the GradientTape API 78
An end-to-end example: A linear classifier in pure TensorFlow 79
3.6 Anatomy of a neural network: Understanding core Keras APIs 84
Layers: The building blocks of deep learning 84
From layers to models 87
The "compile" step: Configuring the learning process 88
Picking a loss function 90
Understanding the fit() method 91
Monitoring loss and metrics on validation data 91
Inference: Using a model after training 93
4 Getting slatted with neural networks: Classification and regression 95
4.1 Classifying movie reviews: A binary classification example 97
The IMDB dataset 97
Preparing the data 98
Building your model 99
Validating your approach 102
Using a trained model to generate predictions on new data 105
Further experiments 105
Wrapping up 106
4.2 Classifying newswires: A multiclass classification example 106
The Reuters dataset 106
Preparing the data 107
Building your model 108
Validating your approach 109
Generating predictions on new data 111
A different way to handle the labels and the loss 112
The importance of having sufficiently large intermediate layers 112
Further experiments 113
Wrapping up 113
4.3 Predicting house prices: A regression example 113
The Boston housing price dataset 114
Preparing the data 114
Building your model 115
Validating your approach using K-fold validation 115
Generating predictions on new data 119
Wrapping up 119
5 Fundamentals of machine learning 121
5.1 Generalization: The goal of machine learning 121
Underfitting and overfitting 122
The nature of generalization in deep learning 127
5.2 Evaluating machine learning models 133
Training, validation, and test sets 133
Beating a common-sense baseline 136
Things to keep in mind about model evaluation 137
5.3 Improving model fit 138
Tuning key gradient descent parameters 138
Leveraging better architecture priors 139
Increasing model capacity 140
5.4 Improving generalization 142
Dataset curation 142
Feature engineering 143
Using early stopping 144
Regularizing your model 145
6 The universal workflow of machine learning 153
6.1 Define the task 155
Frame the problem 155
Collect a dataset 156
Understand your data 160
Choose a measure of success 160
6.2 Develop a model 161
Prepare the data 161
Choose an evaluation protocol 162
Beat a baseline 163
Scale up: Develop a model that overfits 164
Regularize and tune your model 165
6.3 Deploy the model 165
Explain your work to stakeholders and set expectations 165
Ship an inference model 166
Monitor your model in the wild 169
Maintain your model 170
7 Working with Keras: A deep dive 172
7.1 A spectrum of workflows 173
7.2 Different ways to build Keras models 173
The Sequential model 174
The Functional API 176
Subclassing the Model class 182
Mixing and matching different components 184
Remember: Use the right tool for the job 185
7.3 Using built-in training and evaluation loops 185
Writing your own metrics 186
Using callbacks 187
Writing your own callbacks 189
Monitoring and visualization with TensorBoard 190
7.4 Writing your own training and evaluation loops 192
Training versus inference 194
Low-level usage of metrics 195
A complete training and evaluation loop 195
Make it fast with tf.function 197
Leveraging fit() with a custom training loop 198
8 Introduction to deep learning for computer vision 201
8.1 Introduction to convnets 202
The convolution operation 204
The max-pooling operation 209
8.2 Training a convnet from scratch on a small dataset 211
The relevance of deep learning for small-data problems 212
Downloading the data 212
Building the model 215
Data preprocessing 217
Using data augmentation 221
8.3 Leveraging a pretrained model 224
Feature extraction with a pretrained model 225
Fine-tuning a pretrained model 234
9 Advanced deep learning for computer vision 238
9.1 Three essential computer vision tasks 238
9.2 An image segmentation example 240
9.3 Modern convnet architecture patterns 248
Modularity, hierarchy, and reuse 249
Residual connections 251
Batch normalization 255
Depthwise separable convolutions 257
Putting it together: A mini Xception-like model 259
9.4 Interpreting what convnets learn 261
Visualizing intermediate activations 262
Visualizing convnet filters 268
Visualizing heatmaps of class activation 273
10 Deep learning for timeseries 280
10.1 Different kinds of timeseries tasks 280
10.2 A temperature-forecasting example 281
Preparing the data 285
A common-sense, non-machine learning baseline 288
Let's try a basic machine learning model 289
Let's try a ID convolutional model 290
A first recurrent baseline 292
10.3 Understanding recurrent neural networks 293
A recurrent layer in Keras 296
10.4 Advanced use of recurrent neural networks 300
Using recurrent dropout to fight overfitting 300
Stacking recurrent layers 303
Using bidirectional RNNs 304
Going even further 307
11 Deep teaming for text 309
11.1 Natural language processing: The bird's eye view 309
11.2 Preparing text data 311
Text standardization 312
Text splitting (tokenization) 313
Vocabulary indexing 314
Using the TextVectorization layer 316
11.3 Two approaches for representing groups of words: Sets and sequences 319
Preparing the IMDB movie reviews data 320
Processing words as a set: The bag-of-words approach 322
Processing words as a sequence: The sequence model approach 327
11.4 The Transformer architecture 336
Understanding self-attention 337
Multi-head attention 341
The Transformer encoder 342
When to use sequence models over bag-of-words models 349
11.5 Beyond text classification: Sequence-to-sequence learning 350
A machine translation example 351
Sequence-to-sequence learning with RNNs 354
Sequence-to-sequence learning with Transformer 358
12 Generative deep learning 364
12.1 Text generation 366
A brief history of generative deep learning for sequence generation 366
How do you generate sequence data? 367
The importance of the sampling strategy 368
Implementing text generation with Keras 369
A text-generation callback with variable-temperature sampling 372
Wrapping up 376
12.2 DeepDream 376
Implementing DeepDream in Keras 377
Wrapping up 383
12.3 Neural style transfer 383
The content loss 384
The style loss 384
Neural style transfer in Keras 385
Wrapping up 391
12.4 Generating images with variational autoencoders 391
Sampling from latent spaces of images 391
Concept vectors for image editing 393
Variational autoencoders 393
Implementing a VAE with Keras 396
Wrapping up 401
12.5 Introduction to generative adversarial networks 401
A schematic GAN implementation 402
A bag of tricks 403
Getting our hands on the CelebA dataset 404
The discriminator 405
The generator 407
The adversarial-network 408
Wrapping up 410
13 Best practices for the real world 412
13.1 Getting the most out of your models 413
Hyperparameter optimization 413
Model ensembling 420
13.2 Scaling-up model training 421
Speeding up training on GPU with mixed precision 422
Multi-GPU training 425
TPU training 428
14 Conclusions 431
14.1 Key concepts in review 432
Various approaches to AI 432
What makes deep learning special within the field of machine learning 432
How to think about-deep learning 433
Key enabling technologies 434
The universal machine learning workflow 435
Key network architectures 436
The space of possibilities 440
14.2 The limitations of deep learning 442
The risk of anthropomorphizing machine learning models 443
Automatons vs. intelligent agents 445
Local generalization vs. extreme generalization 446
The purpose of intelligence 448
Climbing the spectrum of generalization 449
14.3 Setting the course toward greater generality in AI 450
On the importance of setting the right objective: The shortcut rule 450
A new target 452
14.4 Implementing intelligence: The missing ingredients 454
Intelligence as sensitivity to abstract analogies 454
The two poles of abstraction 455
The missing half of the picture 458
14.5 The future of deep learning 459
Models as programs 460
Blending together deep learning and program synthesis 461
Lifelong learning and modular subroutine reuse 463
The long-term vision 465
14.6 Staying up to date in a fast-moving field 466
Practice on real-world problems using Kaggle 466
Real about the latest developments on arXiv 466
Explore the Keras ecosystem 467
14.7 Final words 467
Index 469
