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 start-to-finish instruction book with complete 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...
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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 start-to-finish instruction book with complete 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.
Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.