从零开始精通TensorFlow深度学习实战指南

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"《Pro Deep Learning with TensorFlow：高级人工智能的数学方法》是一本实用的深度学习指南，由Santanu Pattanayak撰写。本书旨在为读者提供从零开始学习深度学习并将其应用于实际场景的专业技能。作者以Python为平台，采用TensorFlow作为主要工具，帮助读者快速掌握这个强大的技术。本书的核心内容包括深入理解深度学习的基本原理，如神经网络、卷积神经网络（CNN）、循环神经网络（RNN）等，并通过实践案例讲解如何构建和优化这些模型。读者可以跟随书中的步骤，从基础概念如梯度下降和反向传播，到更复杂的模型架构设计和调优技巧，逐步提升自己的深度学习能力。此外，书中还着重介绍了如何利用TensorFlow进行高效的编程实践，包括数据预处理、模型训练、验证和评估等关键环节。对于初学者，它提供了清晰的教程和实例，帮助他们建立起坚实的理论基础；而对于已有一定经验的开发者，它则是一本扩展和深化知识的实用手册。该书不仅适用于专业研究人员和工程师，也适合对人工智能和机器学习感兴趣的学生和爱好者。出版信息包括纸质版ISBN-13：978-1-4842-3095-4和电子版ISBN-13：978-1-4842-3096-1，以及Library of Congress Control Number：2017962327。版权信息显示，所有内容受版权保护，未经许可不得复制或以任何形式进行再利用。《Pro Deep Learning with TensorFlow》是一本全面且深入的深度学习教材，将理论与实践紧密结合，是想要在这个领域取得突破的读者不可多得的参考资料。"

■ IntroduCtIon

What You’ll Learn

The chapters covered in this book are as follows:

Chapter 1 — Mathematical Foundations: In this chapter, all the relevant

mathematical concepts from linear algebra, probability, calculus, optimization,

and machine-learning formulation are discussed in detail to lay the mathematical

foundation required for deep learning. The various concepts are explained with a

focus on their use in the fields of machine learning and deep learning.

Chapter 2 — Introduction to Deep-Learning Concepts and TensorFlow: This

chapter introduces the world of deep learning and discusses its evolution

over the years. The key building blocks of neural networks, along with several

methods of learning, such as the perceptron-learning rule and backpropagation

methods, are discussed in detail. Also, this chapter introduces the paradigm of

TensorFlow coding so that readers are accustomed to the basic syntax before

moving on to more-involved implementations in TensorFlow.

Chapter 3 — Convolutional Neural Networks: This chapter deals with convolutional

neural networks used for image processing. Image processing is a computer

vision issue that has seen a huge boost in performance in the areas of object

recognition and detection, object classification, localization, and segmentation

using convolutional neural networks. The chapter starts by illustrating the

operation of convolution in detail and then moves on to the working principles of

a convolutional neural network. Much emphasis is given to the building blocks of

a convolutional neural network to give the reader the tools needed to experiment

and extend their networks in interesting ways. Further, backpropagation through

convolutional and pooling layers is discussed in detail so that the reader has a

holistic view of the training process of convolutional networks. Also covered in this

chapter are the properties of equivariance and translation invariance, which are

central to the success of convolutional neural networks.

Chapter 4 — Natural Language Processing Using Recurrent Neural Networks: This

chapter deals with natural language processing using deep learning. It starts with

different vector space models for text processing; word-to-vector embedding

models, such as the continuous bag of words method and skip-grams; and then

moves to much more advanced topics that involve recurrent neural networks

(RNN), LSTM, bidirection RNN, and GRU. Language modeling is covered in detail

in this chapter to help the reader utilize these networks in real-world problems

involving the same. Also, the mechanism of backpropagation in cases of RNNs and

LSTM as well vanishing-gradient problems are discussed in much detail.

Chapter 5 — Unsupervised Learning with Restricted Boltzmann Machines and

Auto-encoders: In this chapter, you will learn about unsupervised methods

in deep learning that use restricted Boltzmann machines (RBMs) and auto-

encoders. Also, the chapter will touch upon Bayesian inference and Markov

chain Monte Carlo (MCMC) methods, such as the Metropolis algorithm and

Gibbs sampling, since the RBM training process requires some knowledge of

sampling. Further, this chapter will discuss contrastive divergence, a customized

version of Gibbs sampling that allows for the practical training of RBMs. We will

further discuss how RBMs can be used for collaborative filtering in recommender

systems as well as their use in unsupervised pre-training of deep belief networks

(DBNs).

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从零开始精通TensorFlow深度学习实战指南

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