Price: $42.47
(as of Dec 25,2024 02:09:28 UTC – Details)

ASIN ‏ : ‎ B0CB56KQGL
Publisher ‏ : ‎ Springer; 2nd edition (September 15, 2023)
Publication date ‏ : ‎ September 15, 2023
Language ‏ : ‎ English
File size ‏ : ‎ 40960 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 467 pages

Computational Methods for Deep Learning: Theory, Algorithms, and Implementations (Texts in Computer Science)

Deep learning is a rapidly growing field in artificial intelligence, with applications ranging from image and speech recognition to natural language processing and autonomous vehicles. However, understanding and implementing deep learning algorithms can be challenging, as they often involve complex mathematical concepts and computations.

In the book “Computational Methods for Deep Learning: Theory, Algorithms, and Implementations,” readers are introduced to the fundamental principles of deep learning, along with the mathematical and computational techniques necessary for its implementation. The book covers topics such as neural networks, convolutional neural networks, recurrent neural networks, and reinforcement learning, providing a comprehensive overview of the field.

The text also includes practical examples and exercises to help readers apply the concepts they have learned, making it an invaluable resource for students, researchers, and practitioners in the field of deep learning. Whether you are new to deep learning or looking to deepen your understanding of the subject, this book is a must-read for anyone interested in harnessing the power of deep learning algorithms.
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