Introduction to Deep Learning

by Charniak

ISBN: 9780262039512 | Copyright 2019

Click here to preview

Instructor Requests

Digital Exam/Desk Copy Print Exam/Desk Copy Ancillaries

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.

Expand/Collapse All
Cover (pg. i)
Contents (pg. vi)
Preface (pg. xi)
Chapter 1 Feed-Forward Neural Nets (pg. 1)
Chapter 2 Tensorflow (pg. 29)
Chapter 3 Convolutional Neural Networks (pg. 51)
Chapter 4 Word Embeddings and Recurrent NNs (pg. 71)
Chapter 5 Sequence-to-Sequence Learning (pg. 95)
Chapter 6 Deep Reinforcement Learning (pg. 113)
Chapter 7 Unsupervised Neural-Network Models (pg. 137)
Appendix A Answers to Selected Exercises (pg. 159)
Bibliography (pg. 165)
Index (pg. 169)

Eugene Charniak

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

You must have an instructor account and submit a request to access instructor materials for this book.
Go paperless today! Available online anytime, nothing to download or install.


  • Bookmarking
  • Note taking
  • Highlighting