作者：Giancarlo Zaccone，Re
页数：15,458页
出版社：东南大学出版社
出版日期：2019
ISBN：9787564183264
电子书格式：pdf/epub/txt
内容简介
TensorFlow是谷歌研发的人工智能学习系统，是一个用于数值计算的开源软件库。本书以基础加实践相结合的形式，详细介绍了TensorFlow深度学习算法原理及编程技巧。通读全书，读者不仅可以系统了解深度学习的相关知识，还能对使用TensorFlow进行深度学习算法设计的过程有更深入的理解。
本书特色
TensorFlow是谷歌研发的人工智能学习系统，是一个用于数值计算的开源软件库。本书以基础加实践相结合的形式，详细介绍了TensorFlow深度学习算法原理及编程技巧。通读全书，读者不仅可以系统了解深度学习的相关知识，还能对使用TensorFlow进行深度学习算法设计的过程有更深入的理解。
目录
Preface
Chapter 1: Getting Started with Deep Learning
A soft introduction to machine learning
Supervised learning
Unbalanced data
Unsupervised learning
Reinforcement learning
What is deep learning?
Artificial neural networks
The biological neurons
The artificial neuron
How does an ANN learn?
ANNs and the backpropagation algorithm
Weight optimization
Stochastic gradient descent
Neural network architectures
Deep Neural Networks (DNNs)
Multilayer perceptron
Deep Belief Networks (DBNs)
Convolutional Neural Networks (CNNs)
AutoEncoders
Recurrent Neural Networks (RNNs)
Emergent architectures
Deep learning frameworks
Summary
Chapter 2: A First Look at TensorFlow
A general overview of TensorFlow
What’s new in TensorFlow vl.6?
Nvidia GPU support optimized
Introducing TensorFlow Lite
Eager execution
Optimized Accelerated Linear Algebra (XLA)
Installing and configuring TensorFlow
TensorFlow computational graph
TensorFlow code structure
Eager execution with TensorFIow
Data model in TensorFlow
Tensor
Rank and shape
Data type
Variables
Fetches
Feeds and placeholders
Visualizing computations through TensorBoard
How does TensorBoard work?
Linear regression and beyond
Linear regression revisited for a real dataset
Summary
Chapter 3: Feed-Forward Neural Networks with TensorFIow
Feed-forward neural networks (FFNNs)
Feed-forward and backpropagation
Weights and biases
Activation functions
Using sigmoid
Using tanh
Using ReLU
Using softmax
Implementing a feed-forward neural network
Exploring the MNIST dataset
Softmax classifier
Implementing a multilayer perceptron (MLP)
Training an MLP
Using MLPs
Dataset description
Preprocessing
A TensorFIow implementation of MLP for client-subscription assessment
Chapter 4: Convolutional Neural Networks
Chapter 5: Optimizing TensorFIow Autoencoders
Chapter 6: Recurrent Neural Networks
Chapter 7: Heterogeneous and Distributed Computing
Chapter 8: Advanced TensorFIow Programming
Chapter 9: Recommendation Systems Using Factorization Machines
Chapter 10: Reinforcement Learning
Other Books You May Enjoy
Index
Chapter 1: Getting Started with Deep Learning
A soft introduction to machine learning
Supervised learning
Unbalanced data
Unsupervised learning
Reinforcement learning
What is deep learning?
Artificial neural networks
The biological neurons
The artificial neuron
How does an ANN learn?
ANNs and the backpropagation algorithm
Weight optimization
Stochastic gradient descent
Neural network architectures
Deep Neural Networks (DNNs)
Multilayer perceptron
Deep Belief Networks (DBNs)
Convolutional Neural Networks (CNNs)
AutoEncoders
Recurrent Neural Networks (RNNs)
Emergent architectures
Deep learning frameworks
Summary
Chapter 2: A First Look at TensorFlow
A general overview of TensorFlow
What’s new in TensorFlow vl.6?
Nvidia GPU support optimized
Introducing TensorFlow Lite
Eager execution
Optimized Accelerated Linear Algebra (XLA)
Installing and configuring TensorFlow
TensorFlow computational graph
TensorFlow code structure
Eager execution with TensorFIow
Data model in TensorFlow
Tensor
Rank and shape
Data type
Variables
Fetches
Feeds and placeholders
Visualizing computations through TensorBoard
How does TensorBoard work?
Linear regression and beyond
Linear regression revisited for a real dataset
Summary
Chapter 3: Feed-Forward Neural Networks with TensorFIow
Feed-forward neural networks (FFNNs)
Feed-forward and backpropagation
Weights and biases
Activation functions
Using sigmoid
Using tanh
Using ReLU
Using softmax
Implementing a feed-forward neural network
Exploring the MNIST dataset
Softmax classifier
Implementing a multilayer perceptron (MLP)
Training an MLP
Using MLPs
Dataset description
Preprocessing
A TensorFIow implementation of MLP for client-subscription assessment
Chapter 4: Convolutional Neural Networks
Chapter 5: Optimizing TensorFIow Autoencoders
Chapter 6: Recurrent Neural Networks
Chapter 7: Heterogeneous and Distributed Computing
Chapter 8: Advanced TensorFIow Programming
Chapter 9: Recommendation Systems Using Factorization Machines
Chapter 10: Reinforcement Learning
Other Books You May Enjoy
Index
