Introduction to Deep Learning Hardback
by Eugene (Brown University) Charniak
Part of the The MIT Press series
Hardback
Description
A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning.
The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques.
Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. "I find I learn computer science material best by sitting down and writing programs," the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading.
An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language.
Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python.
The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.
Information
-
Out of stock
- Format:Hardback
- Pages:192 pages, 75 b&w illus.; 150 Illustrations, unspecified
- Publisher:MIT Press Ltd
- Publication Date:29/01/2019
- Category:
- ISBN:9780262039512
Information
-
Out of stock
- Format:Hardback
- Pages:192 pages, 75 b&w illus.; 150 Illustrations, unspecified
- Publisher:MIT Press Ltd
- Publication Date:29/01/2019
- Category:
- ISBN:9780262039512
