Neural Networks and Deep Learning: A Textbook

The book provides a comprehensive introduction to deep learning, covering classical and modern models, theory, and algorithms. It discusses neural network design concepts, training challenges, and applications in various domains. The second edition is expanded and updated with new chapters on backpropagation and graph neural networks.

Format: Hardback
Length: 529 pages
Publication date: 17 July 2023
Publisher: Springer International Publishing AG

This comprehensive book delves into both classical and modern models of deep learning, with a primary emphasis on the theory and algorithms underpinning this powerful technology. The theory and algorithms of neural networks play a crucial role in understanding essential concepts, enabling individuals to grasp the key design principles of neural architectures across diverse applications.

The book addresses various questions related to neural networks, including why they work, when they outperform off-the-shelf machine-learning models, when depth is beneficial, why training neural networks is challenging, and the potential pitfalls to avoid. It also provides a rich discussion of different applications, showcasing how neural architectures are tailored for solving various types of problems.

The book is organized into three categories:

The Basics of Neural Networks: Chapter 2 delves into the backpropagation algorithm, a fundamental concept in neural networks. It explains how errors are propagated through the network and how the weights are updated to minimize these errors. This chapter also explores the connections between traditional machine learning and neural networks, highlighting how many traditional models can be understood as special cases of neural networks.

Fundamentals of Neural Networks: Chapters 4 and 5 provide a detailed discussion of training and regularization, two critical aspects of neural network training. These chapters cover topics such as gradient descent, momentum, and dropout regularization, which are used to improve the convergence and generalization of neural networks. Chapters 6 and 7 introduce radial-basis function (RBF) networks and restricted Boltzmann machines, two popular neural network architectures.

Advanced Topics in Neural Networks: Chapters 8, 9, and 10 explore advanced topics in neural networks, including recurrent neural networks, convolutional neural networks, and graph neural networks. These chapters discuss the latest developments and applications in deep learning, such as deep reinforcement learning, attention mechanisms, and transfer learning.

In addition to its theoretical discussions, the book includes practical examples and code snippets that demonstrate the implementation of deep learning algorithms in various programming languages, including Python, MATLAB, and C++. This hands-on approach allows readers to apply the concepts learned in the book to real-world problems and gain practical experience with deep learning.

Whether you are a researcher, engineer, or practitioner interested in machine learning, this book is an essential resource for understanding and leveraging the power of deep learning. Its comprehensive coverage, practical examples, and accessible writing style make it suitable for a wide range of audiences. So, why wait? Start exploring the world of deep learning with this must-read book today!

Weight: 1230g
Dimension: 254 x 178 (mm)
ISBN-13: 9783031296413
Edition number: 2nd ed. 2023