Introduction to Deep Learning

Introduction to Deep Learning by Eugene Charniak

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.

Charniak, Eugene: - Eugene Charniak is Professor of Computer Science at Brown University. He is the author of Statistical Language Learning (MIT Press) and other books.

SKU	Unavailable
ISBN 13	9780262039512
ISBN 10	0262039516
Title	Introduction to Deep Learning
Author	Eugene Charniak
Series	The Mit Press
Condition	Unavailable
Binding Type	Hardback
Publisher	MIT Press Ltd
Year published	2019-01-29
Number of pages	192
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable