封面
版权信息
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Mission
How deep learning is different from machine learning and artificial intelligence
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
Neural Networks Foundations
Perceptron
The first example of Keras code
Multilayer perceptron - the first example of a network
Problems in training the perceptron and a solution
Activation function - sigmoid
Activation function - ReLU
Activation functions
A real example - recognizing handwritten digits
One-hot encoding - OHE
Defining a simple neural net in Keras
Running a simple Keras net and establishing a baseline
Improving the simple net in Keras with hidden layers
Further improving the simple net in Keras with dropout
Testing different optimizers in Keras
Increasing the number of epochs
Controlling the optimizer learning rate
Increasing the number of internal hidden neurons
Increasing the size of batch computation
Summarizing the experiments run for recognizing handwritten charts
Adopting regularization for avoiding overfitting
Hyperparameters tuning
Predicting output
A practical overview of backpropagation
Towards a deep learning approach
Summary
Keras Installation and API
Installing Keras
Step 1 - install some useful dependencies
Step 2 - install Theano
Step 3 - install TensorFlow
Step 4 - install Keras
Step 5 - testing Theano TensorFlow and Keras
Configuring Keras
Installing Keras on Docker
Installing Keras on Google Cloud ML
Installing Keras on Amazon AWS
Installing Keras on Microsoft Azure
Keras API
Getting started with Keras architecture
What is a tensor?
Composing models in Keras
Sequential composition
Functional composition
An overview of predefined neural network layers
Regular dense
Recurrent neural networks - simple LSTM and GRU
Convolutional and pooling layers
Regularization
Batch normalization
An overview of predefined activation functions
An overview of losses functions
An overview of metrics
An overview of optimizers
Some useful operations
Saving and loading the weights and the architecture of a model
Callbacks for customizing the training process
Checkpointing
Using TensorBoard and Keras
Using Quiver and Keras
Summary
Deep Learning with ConvNets
Deep convolutional neural network - DCNN
Local receptive fields
Shared weights and bias
Pooling layers
Max-pooling
Average pooling
ConvNets summary
An example of DCNN - LeNet
LeNet code in Keras
Understanding the power of deep learning
Recognizing CIFAR-10 images with deep learning
Improving the CIFAR-10 performance with deeper a network
Improving the CIFAR-10 performance with data augmentation
Predicting with CIFAR-10
Very deep convolutional networks for large-scale image recognition
Recognizing cats with a VGG-16 net
Utilizing Keras built-in VGG-16 net module
Recycling pre-built deep learning models for extracting features
Very deep inception-v3 net used for transfer learning
Summary
Generative Adversarial Networks and WaveNet
What is a GAN?
Some GAN applications
Deep convolutional generative adversarial networks
Keras adversarial GANs for forging MNIST
Keras adversarial GANs for forging CIFAR
WaveNet - a generative model for learning how to produce audio
Summary
Word Embeddings
Distributed representations
word2vec
The skip-gram word2vec model
The CBOW word2vec model
Extracting word2vec embeddings from the model
Using third-party implementations of word2vec
Exploring GloVe
Using pre-trained embeddings
Learn embeddings from scratch
Fine-tuning learned embeddings from word2vec
Fine-tune learned embeddings from GloVe
Look up embeddings
Summary
Recurrent Neural Network — RNN
SimpleRNN cells
SimpleRNN with Keras - generating text
RNN topologies
Vanishing and exploding gradients
Long short term memory - LSTM
LSTM with Keras - sentiment analysis
Gated recurrent unit - GRU
GRU with Keras - POS tagging
Bidirectional RNNs
Stateful RNNs
Stateful LSTM with Keras - predicting electricity consumption
Other RNN variants
Summary
Additional Deep Learning Models
Keras functional API
Regression networks
Keras regression example - predicting benzene levels in the air
Unsupervised learning - autoencoders
Keras autoencoder example - sentence vectors
Composing deep networks
Keras example - memory network for question answering
Customizing Keras
Keras example - using the lambda layer
Keras example - building a custom normalization layer
Generative models
Keras example - deep dreaming
Keras example - style transfer
Summary
AI Game Playing
Reinforcement learning
Maximizing future rewards
Q-learning
The deep Q-network as a Q-function
Balancing exploration with exploitation
Experience replay or the value of experience
Example - Keras deep Q-network for catch
The road ahead
Summary
Conclusion
Keras 2.0 - what is new
Installing Keras 2.0
API changes
更新时间：2021-07-02 23:58:32
完结共168章
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