Neural Networks and Deep Learning: An Engaging Textbook (2nd Edition)

Neural Networks and Deep Learning: An Engaging Textbook (2nd Edition)

Introduction:

Welcome to the second edition of “Neural Networks and Deep Learning: A Textbook” by Charu C. Aggarwal. This comprehensive book provides a detailed exploration of neural networks and their applications. The book is divided into three categories: the basics of neural networks, fundamentals of neural networks, and advanced topics in neural networks.

In the basics section, you will find a discussion on the backpropagation algorithm and its connection to traditional machine learning models. The fundamentals section covers training, regularization, radial-basis function networks, and restricted Boltzmann machines. Lastly, the advanced topics section delves into recurrent neural networks, convolutional neural networks, and graph neural networks, along with other cutting-edge concepts.

The second edition has been expanded and reorganized with separate chapters on backpropagation and graph neural networks. It also focuses on modern deep learning ideas such as adversarial learning, attention mechanisms, transformers, and large language models.

You can purchase the hardcopy of the book from various booksellers like Amazon. However, the e-copy in PDF format is exclusively available from Springer. The PDF edition features Kindle-like hyperlinks for easy navigation and can be used on Kindle and mobile devices like the iPad.

Don’t miss out on this invaluable resource for understanding and applying neural networks and deep learning. Get your copy today and unlock the potential of this rapidly evolving field.

Full Article: Neural Networks and Deep Learning: An Engaging Textbook (2nd Edition)

Neural Networks and Deep Learning: A Textbook (2nd Edition) – A Comprehensive Guide to Neural Networks

Neural Networks and Deep Learning: A Textbook (2nd Edition) is a comprehensive guidebook written by Charu C. Aggarwal that covers the essentials, fundamentals, and advanced topics in neural networks. This second edition offers new insights, expanded chapters, and a reorganized structure to provide readers with a thorough understanding of neural networks and deep learning.

Exploring the Basics of Neural Networks

The book dives into the basics of neural networks in its initial chapters. In Chapter 2, the backpropagation algorithm is discussed, offering valuable insights into the workings of neural networks. Additionally, Chapter 3 explores the connections between traditional machine learning and neural networks. It demonstrates that many traditional machine learning models can be seen as special cases of neural networks. For instance, support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and matrix factorization-based recommender systems are all revealed to be special cases of neural networks.

Delving into Fundamentals

To provide readers with a comprehensive understanding, Neural Networks and Deep Learning also delves into the fundamentals of neural networks. Chapters 4 and 5 focus on training and regularization, offering detailed discussions on these crucial aspects. Readers can gain insights into the training processes and learn how to effectively regularize neural networks. Furthermore, Chapters 6 and 7 introduce radial-basis function (RBF) networks and restricted Boltzmann machines, respectively, expanding on the fundamentals of neural networks.

Advanced Topics Explored

Neural Networks and Deep Learning goes beyond the basics to explore advanced topics in neural networks. Chapters 8, 9, and 10 focus on recurrent neural networks, convolutional neural networks, and graph neural networks, respectively. The book also introduces several advanced topics such as deep reinforcement learning, attention mechanisms, transformer networks, large language models, Kohonen’s self-organizing maps, and generative adversarial networks in Chapters 11 and 12.

Enhanced Second Edition

The second edition of Neural Networks and Deep Learning has been substantially reorganized and expanded. With separate chapters on backpropagation and graph neural networks, this edition offers a more comprehensive coverage of these important topics. Many chapters have also been significantly revised from the first edition to provide readers with the latest insights and developments in the field. The second edition places greater emphasis on modern deep learning ideas like adversarial learning, graph neural networks, attention mechanisms, transformers, and large language models.

Availability of the Book

Neural Networks and Deep Learning is available in both hardcopy and e-copy formats. The hardcopy is available from various booksellers such as Amazon. However, the e-copy of the book is exclusively available from Springer as a PDF. The PDF version has Kindle-like hyperlinks for easy navigation and can be used on Kindle and mobile devices like the iPad. A Kindle edition is expected to be released in the near future, further expanding the accessibility of the book.

Conclusion

Neural Networks and Deep Learning: A Textbook (2nd Edition) by Charu C. Aggarwal provides readers with a comprehensive understanding of neural networks. With its coverage of the fundamentals, basics, and advanced topics in neural networks, this book is suitable for beginners and experienced practitioners alike. The enhanced second edition offers expanded and revised chapters, focusing on modern deep learning concepts. Whether in hardcopy or e-copy format, this comprehensive guidebook is a valuable resource for those interested in neural networks and deep learning.

Summary: Neural Networks and Deep Learning: An Engaging Textbook (2nd Edition)

“The second edition of ‘Neural Networks and Deep Learning: A Textbook’ by Charu C. Aggarwal offers comprehensive coverage of neural networks, from the basics to advanced topics. The book explores the connections between traditional machine learning models and neural networks, providing insights into various techniques. It also delves into modern deep learning concepts like graph neural networks, attention mechanisms, transformers, and large language models. With a focus on practical applications, this updated edition is organized and expanded to include separate chapters on backpropagation and graph neural networks. The book is available in both hardcopy and PDF formats, with the PDF version featuring Kindle-like hyperlinks for easy navigation.”

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