Deep Neural Networks in a Mathematical Framework (SpringerBriefs in Computer Science)

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(as of Dec 24,2024 13:08:14 UTC – Details)

Publisher ‏ : ‎ Springer; 1st ed. 2018 edition (April 3, 2018)
Language ‏ : ‎ English
Paperback ‏ : ‎ 97 pages
ISBN-10 ‏ : ‎ 3319753037
ISBN-13 ‏ : ‎ 978-3319753034
Item Weight ‏ : ‎ 1 pounds
Dimensions ‏ : ‎ 6.1 x 0.23 x 9.25 inches

Deep Neural Networks in a Mathematical Framework (SpringerBriefs in Computer Science)

In this post, we will explore the book “Deep Neural Networks in a Mathematical Framework” from the SpringerBriefs in Computer Science series. This book provides a comprehensive overview of deep neural networks from a mathematical perspective, making it an essential read for researchers, students, and practitioners in the field of machine learning and artificial intelligence.

The book covers various aspects of deep neural networks, including their architecture, training methods, optimization techniques, and applications. It also delves into the mathematical foundations of deep learning, such as linear algebra, calculus, probability theory, and optimization theory.

With a focus on understanding the mathematical principles behind deep neural networks, this book aims to demystify the complex algorithms and techniques used in modern machine learning systems. By providing a rigorous mathematical framework, readers can gain a deeper insight into the inner workings of deep learning models and develop a solid foundation for further research and experimentation.

Whether you are a beginner looking to understand the basics of deep learning or an experienced practitioner seeking to enhance your knowledge, “Deep Neural Networks in a Mathematical Framework” offers valuable insights and practical guidance for mastering this cutting-edge technology. Get your copy today and embark on a journey into the fascinating world of deep neural networks.
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