TensorFlow深度学习实战：构建高级AI的数学方法

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"《深度学习实战：TensorFlow指南》是一本实用且系统的学习资源，专为想要深入了解和应用深度学习的人设计。作者Sanatanu Pattanayak以数学方法为基石，带领读者从零开始掌握高级人工智能中的深度学习技术，并特别聚焦于Python编程环境中的TensorFlow。本书不仅提供了丰富的实践指导，让读者快速上手TensorFlow，还强调了理论与实践相结合的重要性，帮助读者理解并创新深度学习架构。通过阅读这本书，读者将学习到一系列关键知识点，包括但不限于： 1. TensorFlow基础知识：深入理解TensorFlow的工作原理，掌握其核心API，如张量操作、计算图构建和会话管理。 2. 深度学习数学基础：介绍神经网络背后的数学原理，如线性代数、微积分和概率论在深度学习中的应用，这对于理解深层模型的运作至关重要。 3. 深度学习模型构建：从卷积神经网络（CNN）、循环神经网络（RNN）到递归神经网络（Recursive Neural Networks），以及自编码器（Autoencoders）等各类模型的实现策略。 4. 优化算法：掌握梯度下降法、Adam优化器等常用优化算法，以及如何调整学习率以提高模型性能。 5. 深度学习项目实战：通过实际案例，学习如何部署深度学习模型解决实际问题，如图像分类、自然语言处理或推荐系统等。 6. 生产环境部署：了解如何将深度学习模型从开发阶段迁移到生产环境，涉及模型部署、监控和维护的最佳实践。 7. 创新与扩展：激发读者的创新思维，学会如何根据需求定制深度学习架构，以及如何适应不断发展的技术趋势。《深度学习实战：TensorFlow指南》不仅是技术教程，也是对深度学习领域理论的深入探讨，旨在培养读者成为能够独立发明和实施深度学习解决方案的专业人士。无论你是初学者还是经验丰富的工程师，本书都将为你的职业发展提供坚实的基础。"

LYW

■ 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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