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zhang lipton z dive deep learning 2023

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Textbook in PDF format This book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The book can be divided into roughly three parts, focusing on preliminaries, deep learning techniques, and advanced topics focused on real systems and applications. Part 1: Basics and Preliminaries. Chapter 1 oﬀers an introduction to deep learning. Then, in Chapter 2, we quickly bring you up to speed on the prerequisites required for hands-on deep learning, such as how to store and manipulate data, and how to apply various numerical operations based on basic concepts from linear algebra, calculus, and probability. Chapter 3 and Chapter 5 cover the most basic concepts and techniques in deep learning, including regression and classiﬁcation; linear models; multilayer perceptrons; and overﬁtting and regularization. Part 2: Modern Deep Learning Techniques. Chapter 6 describes the key computational components of deep learning systems and lays the groundwork for our subsequent implementations of more complex models. Next, Chapter 7 and Chapter 8 introduce convolutional neural networks (CNNs), powerful tools that form the backbone of most modern computer vision systems. Similarly, Chapter 9 and Chapter 10 introduce recurrent neural networks (RNNs), models that exploit sequential (e.g., temporal) structure in data and are commonly used for natural language processing and time series prediction. In Chapter 11, we introduce a relatively new class of models based on so-called attention mechanisms that has displaced RNNs as the dominant architecture for most natural language processing tasks. These sections will bring you up to speed on the most powerful and general tools that are widely used by deep learning practitioners. Part 3: Scalability, Eﬃciency, and Applications. In Chapter 12, we discuss several common optimization algorithms used to train deep learning models. Next, in Chapter 13, we examine several key factors that inﬂuence the computational performance of deep learning code. Then, in Chapter 14, we illustrate major applications of deep learning in computer vision. Finally, in Chapter 15 and Chapter 16, we demonstrate how to pretrain language representation models and apply them to natural language processing tasks. Most of the code in this book is based on PyTorch, an extremely popular open-source framework that has been enthusiastically embraced by the deep learning research community. All of the code in this book has passed tests under the latest stable version of PyTorch. This book is for students (undergraduate or graduate), engineers, and researchers, who seek a solid grasp of the practical techniques of deep learning. Because we explain every concept from scratch, no previous background in deep learning or machine learning is required. Fully explaining the methods of deep learning requires some mathematics and programming, but we will only assume that you come in with some basics, including modest amounts of linear algebra, calculus, probability, and Python programming. Just in case you forget the basics, the online Appendix provides a refresher on most of the mathematics you will ﬁnd in this book

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zhang lipton z dive deep learning 2023

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