An approachable text combining the depth and quality of a textbook with the interactive multi-framework code of a hands-on tutorial.

Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse ?elds as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for ?tting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required-every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.

Autorentext
Aston Zhang is Senior Scientist at Amazon Web Services. Zachary C. Lipton is Assistant Professor of Machine Learning and Operations Research at Carnegie Mellon University.Mu Li is Senior Principal Scientist at Amazon Web Services.Alexander J. Smola is VP/Distinguished Scientist for Machine Learning at Amazon Web Services.

Zusammenfassung
This approachable text teaches all the concepts, the context, and the code needed to understand deep learning. Suitable for students and professionals, the book doesn't require any previous background in machine learning or deep learning. Interactive examples feature throughout, with runnable code and executable Jupyter notebooks available online.

Inhalt
Installation; Notation; 1. Introduction; 2. Preliminaries; 3. Linear neural networks for regression; 4. Linear neural networks for classification; 5. Multilayer perceptrons; 6. Builders guide; 7. Convolutional neural networks; 8. Modern convolutional neural networks; 9. Recurrent neural networks; 10. Modern recurrent neural networks; 11. Attention mechanisms and transformers; Appendix. Tools for deep learning; Bibliography; Index.