TensorFlow深度学习指南：Python中的人工智能数学方法

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"《Pro Deep Learning with TensorFlow：Python中的先进人工智能数学方法》是一本专为想要深入了解机器学习和深度学习领域的读者设计的权威指南。作者Santhanu Pattanayak以数学为核心，结合Python实践，深入剖析了TensorFlow这一在AI领域广受欢迎的工具。本书从基础的数学概念出发，例如矩阵和概率论，逐步引导读者掌握深度学习的基本单元——全连接网络，以及各类先进的神经网络结构，包括卷积神经网络（CNN）和循环神经网络（RNN）。对于自然语言处理（NLP），书中详细介绍了各种模型，展示了它们如何应用于实际场景。此外，作者还探讨了近年来备受瞩目的生成对抗网络（GAN）技术，展示了其在生成逼真数据方面的潜力。每个概念都配以Python代码示例，使得理论与实践紧密结合，便于读者理解和应用。《Pro Deep Learning with TensorFlow》不仅适合对深度学习有兴趣的初学者，也适合已有一定基础的开发者，因为它涵盖了从入门到进阶的知识，无论你是希望系统学习还是提升技能，都能从中获益匪浅。该书的纸质版和电子版均有ISBN号，且已获得版权保护，确保了知识的合法传播。通过阅读本书，读者将能够建立起坚实的数学基础，掌握TensorFlow的使用技巧，从而在这个快速发展的领域中保持竞争力。如果你对人工智能的数学原理、深度学习技术以及如何在Python环境下实施它们感兴趣，那么这本书无疑是你不可或缺的参考资料。"

■ 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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