TensorFlow深度学习高级数学方法指南：Santanu Pattanayak解构

需积分: 0 17 浏览量更新于2024-06-30 收藏 13.53MB PDF 举报

"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

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).

剩余411页未读，继续阅读

小埋妹妹

粉丝: 30
资源: 343

TensorFlow深度学习高级数学方法指南：Santanu Pattanayak解构

Pro Deep Learning with TensorFlow

Pro Deep Learning with TensorFlow.

RunRunUsanta:跑跑乌桑塔

凯文凯利耶桑塔（Kevinkellyisyanta）

桑塔露琪亚欣赏课教案.doc

桑塔露琪亚教学设计及反思.doc

《桑塔露琪亚》说课稿.ppt

培训桑塔是综合收益最高的.pptx

桑塔露琪亚 大汤普森版钢琴曲谱.pdf

《桑塔露琪亚》教学设计参照.doc

最新资源

桑塔露琪亚大汤普森版钢琴曲谱.pdf