TensorFlow深度学习进阶指南：Python实现高级人工智能

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"Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python" 是一本由 Santanu Pattanayak 编写的实用教程，专为那些希望深入了解和掌握TensorFlow编程以及深度学习技术的读者设计。本书旨在通过深入数学方法，引导读者利用Python这一强大的工具，进行高级人工智能项目开发。作者以Bangalore, Karnataka, India为背景，强调了该书在实际应用中的价值。这本书提供了丰富的学习资源，包括硬拷贝(ISBN-13 pbk: 978-1-4842-3095-4)和电子版(ISBN-13 electronic: 978-1-4842-3096-1)，并附有DOI号：10.1007/978-1-4842-3096-1。此外，它还获得了美国国会图书馆的Control Number: 2017962327，表明其已获得版权保护，所有权利归Santanu Pattanayak所有。书中内容涵盖了TensorFlow的基础到高级特性，包括但不限于神经网络、卷积神经网络（CNN）、循环神经网络（RNN）等深度学习架构的实现与优化。读者将学到如何构建高效的模型，处理大规模数据集，以及如何调试和调整模型以提升性能。此外，作者还特别关注数学理论在深度学习中的应用，确保读者理解每个概念背后的原理。《Pro Deep Learning with TensorFlow》不仅是一本技术手册，还是一本教学资源，适合对人工智能和机器学习感兴趣的学生、工程师以及研究人员。通过阅读这本书，读者不仅能提升编程技能，还能紧跟AI领域的最新发展，从而在实际工作中实现更高效、更智能的解决方案。对于想要在这个快速发展的领域取得成功的人来说，这本书无疑是一个宝贵的指南。

■ 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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