尤金·查尔尼克：深度学习导论 Introduction to Deep Learning

基本信息

Series:The MIT Press

Format:Hardback 192 pages, 75 b&w illus.; 150 Illustrations, unspecified

Publisher:MIT Press Ltd

Imprint:MIT Press

ISBN:9780262039512

Published:29 Jan 2019

Weight:540g

Dimensions:185 x 235 x 23 (mm)

页面参数仅供参考，具体以实物为准

书籍简介

一本基于项目的深度学习基础指南。

这本简明扼要、以项目为导向的深度学习指南带领读者完成一系列程序编写任务，向他们介绍深度学习在计算机视觉、自然语言处理和强化学习等人工智能领域的应用。作者是一位长期从事自然语言处理的人工智能研究员，他的内容包括前馈神经网、卷积神经网、词嵌入、递归神经网、序列到序列学习、深度强化学习、无监督模型以及其他基本概念和技术。学生和从业人员通过在Tensorflow（一个开源的机器学习框架）中的程序来学习深度学习的基础知识。作者写道："我发现我学习计算机科学材料的方式是坐下来写程序，"本书也反映了这种方法。

每一章都包括一个编程项目、练习，以及进一步阅读的参考资料。早期的一章专门介绍了Tensorflow及其与Python（一种广泛使用的编程语言）的接口。熟悉线性代数、多元微积分、概率和统计学是必要的，也需要有Python编程的基本知识。本书可用于本科生和研究生课程；从业者会发现它是一本必不可少的参考书。

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.

作者简介

尤金·查尼亚克是布朗大学的计算机科学教授。他是《统计语言学习》（MIT出版社）和其他书籍的作者。

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