深度学习是基于学习多层次抽象的机器学习算法的一个分支。作为深度学习核心的神经网络被用于预测分析、计算机视觉、自然语言处理、时间序列预测以及执行大量其他的复杂任务。
本书面向的是希望利用TensorFlow的强大功能，结合其他的开源Python库构建强大、稳健、准确的预测模型的开发人员、数据分析师、机器学习从业者和深度学习爱好者。
在本书中，你将学习如何使用前馈神经网络、卷积神经网络、递归神经网络、自动编码器和因式分解机为机器学习系统开发深度学习应用程序，了解如何以分布式的方式在GPU上完成深度学习编程。
最终，你将深入了解机器学习技术以及将其应用于现实项目的技巧。

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
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Index