Python深度学习算法实践(影印版)(英文版)

本书深入浅出地剖析了 深度学习的原理和相关技术 。书中使用Python，从基本 的数学知识出发，带领读者 从零创建一个经典的深度学 习网络，使读者在此过程中 逐步理解深度学习。书中不 仅介绍了深度学习和神经网 络的概念、特征等基础知识 ，对误差反向传播法、卷积 神经网络等也有深入讲解， 此外还介绍了深度学习相关 的实用技巧，自动驾驶、图 像生成、强化学习等方面的 应用，以及为什么加深层可 以提高识别精度等疑难的问 题。

Preface Section 1" Getting Started with Deep Learning Chapter 1: Introduction to Deep Learning What is deep learning? Biological and artificial neurons ANN and its layers Input layer Hidden layer Output layer Exploring activation functions The sigmoid function The tanh function The Rectified Linear Unit function The leaky ReLU function The Exponential linear unit function The Swish function The softmax function Forward propagation in ANN How does ANN learn? Debugging gradient descent with gradient checking Putting it all together Building a neural network from scratch Summary Questions Further reading Chapter 2: Getting to Know TensorFIow What is TensorFIow? Understanding computational graphs and sessions Sessions Variables, constants, and placeholders Variables Constants Placeholders and feed dictionaries Introducing TensorBoard Creating a name scope Handwritten digit classification using TensorFIow Importing the required libraries Loading the dataset Defining the number of neurons in each layer Defining placeholders Forward propagation Computing loss and backpropagation Computing accuracy Creating summary Training the model Visualizing graphs in TensorBoard Introducing eager execution Math operations in TensorFIow TensorFIow 2.0 and Keras Bonjour Keras