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Test Bank for Deep Learning with Python (1st Edition) by Francois Chollet

By: Francois Chollet
ISBN-10: 1617294438
/ ISBN-13: 9781617294433

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Format: Downloadable ZIP Fille
Authors: Francois Chollet
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Table of content:

Preface xiii
Acknowledgments xv
About this book xvi
About the author xx
About the cover xxi
Part 1 Fundamentals of deep learning 1
1 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 modern machine-learning landscape 18
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
2 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 (1D 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 37
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
3 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
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
4 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 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 luning your hyperparameters 114
4.6 Chapter summary 116
Part 2 Deep Learning in Practice 117
5 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-luning 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
6 Deep learning for text and sequences 178
6.1 Working with text data 180
One-hot encoding of words 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
A common-sense, non-machine-learning baseline 212
A basic machine-learning approach 213
A first recurrent baseline 215
Using recurrent dropout 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 1D convolution for sequence data 225
1D pooling for sequence data 226
Implementing a 1D convnet 226
Combining CNNs and RNNs to process long sequences 228
Wrapping up 231
6.5 Chapter summary 232
7 Advanced deep-learning best practices 233
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
8 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
8.4 Generating images with variational autoencoders 296
Sampling from latent spaces of images 296
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
The discriminator 309
The adversarial network 310
How to train your DCGAN 310
Wrapping up 312
8.6 Chapter summary 313
9 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 327
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 337
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 Running Jupyter notebooks on an EC2 GPU instance 345
Index 35µµ
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