PythonDeepLearning - (EPUB全文下载)

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书籍内容：

Python Deep Learning
Table of Contents
Python Deep Learning
Credits
About the Authors
About the Reviewer
www.PacktPub.com
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Why subscribe?
Customer Feedback
Preface
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
1. Machine Learning – An Introduction
What is machine learning?
Different machine learning approaches
Supervised learning
Unsupervised learning
Reinforcement learning
Steps Involved in machine learning systems
Brief description of popular techniques/algorithms
Linear regression
Decision trees
K-means
Naïve Bayes
Support vector machines
The cross-entropy method
Neural networks
Deep learning
Applications in real life
A popular open source package
Summary
2. Neural Networks
Why neural networks?
Fundamentals
Neurons and layers
Different types of activation function
The back-propagation algorithm
Linear regression
Logistic regression
Back-propagation
Applications in industry
Signal processing
Medical
Autonomous car driving
Business
Pattern recognition
Speech production
Code example of a neural network for the function xor
Summary
3. Deep Learning Fundamentals
What is deep learning?
Fundamental concepts
Feature learning
Deep learning algorithms
Deep learning applications
Speech recognition
Object recognition and classification
GPU versus CPU
Popular open source libraries – an introduction
Theano
TensorFlow
Keras
Sample deep neural net code using Keras
Summary
4. Unsupervised Feature Learning
Autoencoders
Network design
Regularization techniques for autoencoders
Denoising autoencoders
Contractive autoencoders
Sparse autoencoders
Summary of autoencoders
Restricted Boltzmann machines
Hopfield networks and Boltzmann machines
Boltzmann machine
Restricted Boltzmann machine
Implementation in TensorFlow
Deep belief networks
Summary
5. Image Recognition
Similarities between artificial and biological models
Intuition and justification
Convolutional layers
Stride and padding in convolutional layers
Pooling layers
Dropout
Convolutional layers in deep learning
Convolutional layers in Theano
A convolutional layer example with Keras to recognize digits
A convolutional layer example with Keras for cifar10
Pre-training
Summary
6. Recurrent Neural Networks and Language Models
Recurrent neural networks
RNN — how to implement and train
Backpropagation through time
Vanishing and exp ............

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