Introduction to Deep Learning (The MIT Press)

Eugene Charniak

  • 出版商: MIT
  • 出版日期: 2019-01-29
  • 售價: $1,225
  • 貴賓價: 9.8$1,201
  • 語言: 英文
  • 頁數: 192
  • 裝訂: Hardcover
  • ISBN: 0262039516
  • ISBN-13: 9780262039512
  • 相關分類: DeepLearning
  • 相關翻譯: 深度學習導論 (簡中版)
    深度學習導論 精裝版 (簡中版)
  • 立即出貨(限量) (庫存=6)

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商品描述

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.

商品描述(中文翻譯)

一本以專案為基礎的深度學習基礎指南。

這本簡潔的、以專案為導向的深度學習指南,通過一系列的程式撰寫任務,引導讀者進入深度學習在人工智慧領域中的應用,如計算機視覺、自然語言處理和強化學習。作者是一位長期從事自然語言處理的人工智慧研究者,涵蓋了前饋神經網絡、卷積神經網絡、詞嵌入、循環神經網絡、序列到序列學習、深度強化學習、無監督模型和其他基本概念和技術。學生和從業人員通過在Tensorflow中進行程式開發來學習深度學習的基礎。作者寫道:“我發現通過坐下來寫程式來學習計算機科學材料是最好的方法”,這本書反映了這種方法。

每章包括一個程式開發專案、練習題和進一步閱讀的參考資料。早期的一章專門介紹了Tensorflow及其與廣泛使用的Python程式語言的接口。需要熟悉線性代數、多變量微積分和概率統計,以及基本的Python程式設計知識。本書可用於本科和研究生課程;從業人員將發現它是一本必不可少的參考書。