Dive into Deep Learning
Published on 7. December 2023
Book
Paperback/Softback
574 pages
978-1-009-38943-3 (ISBN)
Description
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.
Reviews / Votes
'In less than a decade, the AI revolution has swept from research labs to broad industries to every corner of our daily life. Dive into Deep Learning is an excellent text on deep learning and deserves attention from anyone who wants to learn why deep learning has ignited the AI revolution: the most powerful technology force of our time.' Jensen Huang, Founder and CEO, NVIDIA 'This is a timely, fascinating book, providing not only a comprehensive overview of deep learning principles but also detailed algorithms with hands-on programming code, and moreover, a state-of-the-art introduction to deep learning in computer vision and natural language processing. Dive into this book if you want to dive into deep learning!' Jiawei Han, Michael Aiken Chair Professor, University of Illinois at Urbana-Champaign 'This is a highly welcome addition to the machine learning literature, with a focus on hands-on experience implemented via the integration of Jupyter notebooks. Students of deep learning should find this invaluable to become proficient in this field.' Bernhard Schoelkopf,, Director, Max Planck Institute for Intelligent Systems 'Dive into Deep Learning strikes an excellent balance between hands-on learning and in-depth explanation. I've used it in my deep learning course and recommend it to anyone who wants to develop a thorough and practical understanding of deep learning.' Colin Raffel, Assistant Professor, University of North Carolina, Chapel HillMore details
Language
English
Place of publication
Cambridge
United Kingdom
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises
Dimensions
Height: 254 mm
Width: 203 mm
Thickness: 32 mm
Weight
1231 gr
ISBN-13
978-1-009-38943-3 (9781009389433)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.
Content
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.