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

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  • ISBN-10:  1617294438 / ISBN-13:  9781617294433
  • Ebook Details

    • Edition: 1th edition
    • Format: Downloadable ZIP Fille
    • Resource Type : Testbank
    • Publication: 2017
    • Duration: Unlimited downloads
    • Delivery: Instant Download
     

    $35.00 $30.00

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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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