Deep Learning: A Practical Introduction
Deep Learning: A Practical Introduction by Manel Martinez-Ramon, Meenu Ajith, Aswathy Rajendra Kurup
English | July 15, 2024 | ISBN: 1119861861 | 416 pages | MOBI | 32 Mb
English | July 15, 2024 | ISBN: 1119861861 | 416 pages | MOBI | 32 Mb
An engaging and accessible introduction to deep learning perfect for students and professionals
In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples.
Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find:
- Thorough introductions to deep learning and deep learning tools
- Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures
- Practical discussions of recurrent neural networks and non-supervised approaches to deep learning
- Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks
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