The science of deep learning
By: Drori, Iddo.
Publisher: Cambridge Cambridge University Press 2023Description: xxii, 338p.ISBN: 9781108835084.Subject(s): Communications and signal processing | Computer science engineering | Machine learning | Pattern recognitionDDC classification: 006.31 | D837s Summary: The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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Books | PK Kelkar Library, IIT Kanpur | General Stacks | 006.31 D837s (Browse shelf) | Checked out to SIDDHARTHA U (S2220226300) | 09/10/2024 | A186281 |
Browsing PK Kelkar Library, IIT Kanpur Shelves , Collection code: General Stacks Close shelf browser
006.31 C423l2 Learning from data | 006.31 C55p Principles and theory for data mining and machine learning | 006.31 C868I An introduction to support vector machines | 006.31 D837s The science of deep learning | 006.31 F775a Applied machine learning | 006.31 G286 Genetic algorithms and genetic programming | 006.31 G286K GENETIC PROGRAMMING III |
The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
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