更新时间：2021-07-02 23:58:32
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
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
