更新时间：2021-06-30 19:18:45
coverpage
Title Page
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Contributors
About the authors
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
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Conventions used
Get in touch
Reviews
Why Deep Learning?
What is AI and deep learning?
The history and rise of deep learning
Why deep learning?
Advantages over traditional shallow methods
Impact of deep learning
The motivation of deep architecture
The neural viewpoint
The representation viewpoint
Distributed feature representation
Hierarchical feature representation
Applications
Lucrative applications
Success stories
Deep learning for business
Future potential and challenges
Summary
Getting Yourself Ready for Deep Learning
Basics of linear algebra
Data representation
Data operations
Matrix properties
Deep learning with GPU
Deep learning hardware guide
CPU cores
CPU cache size
RAM size
Hard drive
Cooling systems
Deep learning software frameworks
TensorFlow – a deep learning library
Caffe
MXNet
Torch
Theano
Microsoft Cognitive Toolkit
Keras
Framework comparison
Setting up deep learning on AWS
Setup from scratch
Setup using Docker
Getting Started with Neural Networks
Multilayer perceptrons
The input layer
The output layer
Hidden layers
Activation functions
Sigmoid or logistic function
Tanh or hyperbolic tangent function
ReLU
Leaky ReLU and maxout
Softmax
Choosing the right activation function
How a network learns
Weight initialization
Forward propagation
Backpropagation
Calculating errors
Updating the network
Automatic differentiation
Vanishing and exploding gradients
Optimization algorithms
Regularization
Deep learning models
Convolutional Neural Networks
Convolution
Pooling/subsampling
Fully connected layer
Overall
Restricted Boltzmann Machines
Energy function
Encoding and decoding
Contrastive divergence (CD-k)
Stacked/continuous RBM
RBM versus Boltzmann Machines
Recurrent neural networks (RNN/LSTM)
Cells in RNN and unrolling
Backpropagation through time
Vanishing gradient and LTSM
Cells and gates in LTSM
