TensorFlow深度学习实战：从入门到精通

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"《Pro Deep Learning with TensorFlow》是一本实用的深度学习指南，由Santanu Pattanayak撰写，旨在帮助读者掌握深度学习的核心概念并将其应用于实际生产环境。本书强调了数学基础在深度学习中的关键作用，通过Python语言提供了一种先进人工智能的数学方法。它不仅教授从零开始学习深度学习的基础知识，还着重于理解深度学习架构的设计和优化技巧。该书的内容结构系统，包括理论与实践相结合，确保读者不仅能快速上手使用TensorFlow，还能深入理解其背后的科学原理。书中包含了大量的示例代码，以iPython笔记本和脚本的形式提供，鼓励读者动手尝试，探索创新的深度学习应用。无论是工业界的专业人士还是对AI感兴趣的初学者，都能从中受益，提升在该领域的创新能力。此外，作者还提供了必要的版权和使用许可信息，强调所有内容受到法律保护，尊重知识产权。对于那些希望在研究领域有所贡献的读者，这本书将为他们奠定坚实的数学和科学基础，让他们能够在社区中分享自己的研究成果。《Pro Deep Learning with TensorFlow》是一本理想的深度学习教材，适合那些希望通过数学方法深入了解人工智能，特别是使用TensorFlow技术进行高级应用的读者。无论是作为学习资料，还是作为专业开发者的参考工具，这本书都能满足不同层次的学习者需求，推动他们在深度学习领域取得突破。"

■ 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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