TensorFlow深度学习高级数学方法指南：Santanu Pattanayak解构
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"Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python" 是一本由桑塔努·帕坦纳亚克所著的专业书籍，它深度探讨了在Python环境下利用TensorFlow进行高级深度学习的方法。这本书旨在为读者提供一个坚实的数学基础，以便他们能够理解和构建复杂的深度学习模型，从而推进人工智能领域的技术进步。
作者以清晰的数学视角来剖析深度学习的核心原理，使读者能够从理论层面深入理解神经网络的工作机制，包括卷积神经网络、循环神经网络等核心架构。通过本书，读者不仅能掌握如何使用TensorFlow库，还能学习到如何设计和优化深度学习模型，以及如何处理实际问题中的数据预处理、模型训练和评估等关键步骤。
书中涵盖了广泛的实践案例，这些案例涵盖了图像识别、自然语言处理、推荐系统等多个应用领域，帮助读者将理论知识转化为实际技能。此外，书中的每个章节都包含了大量的示例代码，便于读者跟随学习，并且提供了详细的解释，使得即使是初学者也能逐步上手。
出版信息方面，这本书在全球范围内发行，包括纸质版(ISBN-13: 978-1-4842-3095-4)和电子版(ISBN-13: 978-1-4842-3096-1)，并且已经获得了美国国会图书馆的控制号(2017962327)。版权方面，所有内容受到严格的版权保护，禁止未经许可的复制、传播或改编。
对于那些希望在人工智能领域深入探索或者从事实际项目开发的读者来说，这是一本不可或缺的参考书籍。它不仅提供了实用的技术指导，还强调了理论与实践相结合的重要性，对于推动深度学习在实际场景中的应用具有重要的价值。无论是学术研究者还是工业界工程师，都可以从中受益匪浅。
■ IntroduCtIon
xx
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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