Deep Learning: A Visual Approach (Paperback)

Glassner, Andrew

  • 出版商: No Starch Press
  • 出版日期: 2021-06-29
  • 定價: $3,400
  • 售價: 9.5$3,230
  • 貴賓價: 9.0$3,060
  • 語言: 英文
  • 頁數: 776
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1718500726
  • ISBN-13: 9781718500723
  • 相關分類: DeepLearning
  • 立即出貨 (庫存 < 3)

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商品描述

A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math.

Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare.

Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless.

Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going.

The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including:


- How text generators create novel stories and articles
- How deep learning systems learn to play and win at human games
- How image classification systems identify objects or people in a photo
- How to think about probabilities in a way that's useful to everyday life
- How to use the machine learning techniques that form the core of modern AI

Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it.

Full Color Illustrations

商品描述(中文翻譯)

一本豐富插圖、全彩的深度學習入門書,以視覺和概念解釋代替方程式。您將學習如何使用關鍵的深度學習演算法,而無需複雜的數學知識。

自從電腦開始在國際象棋中戰勝我們以來,它們在各種人類活動中變得越來越強大,從創作歌曲和生成新聞文章到幫助醫生提供醫療保健。

深度學習是這些突破的源頭,它在數據中發現隱藏的模式的能力使其成為人工智能(AI)領域中增長最快的領域。我們手機上的數字助手使用深度學習來理解和智能回應語音指令;汽車系統使用它來安全地避開道路障礙;在線平台使用它來提供個性化的電影和書籍建議 - 可能性是無窮的。

《深度學習:視覺方法》適合任何想深入了解這個迷人領域的人,但不需要掌握其內部的高級數學和編程知識。如果您想知道這些工具如何工作並自己使用它們,答案都在這些頁面中。而且,如果您準備好撰寫自己的程式,附帶的 Github 存儲庫中還有許多補充的 Python 筆記本可供您使用。

本書以對話風格、豐富的彩色插圖、啟發性的類比和現實世界的例子,專業地解釋了深度學習的關鍵概念,包括:

- 文本生成器如何創作新故事和文章
- 深度學習系統如何學習並贏得人類遊戲
- 影像分類系統如何識別照片中的物體或人物
- 如何以對日常生活有用的方式思考概率
- 如何使用構成現代人工智能核心的機器學習技術

各種智力冒險家都可以利用《深度學習:視覺方法》中涵蓋的強大思想來建立幫助我們更好地理解世界和其中每個人的智能系統。這是人工智能的未來,而這本書讓您完全構想出來。

全彩插圖

作者簡介

Andrew Glassner is a research scientist specializing in computer graphics and deep learning. He is currently a Senior Research Scientist at Weta Digital, where he works on integrating deep learning with the production of world-class visual effects for films and television. He has previously worked as a researcher at labs such as the IBM Watson Lab, Xerox PARC, and Microsoft Research. He was Editor in Chief of ACM TOG, the premier research journal in graphics, and Technical Papers Chair for SIGGRAPH, the premier conference in graphics. He's written or edited a dozen technical books on computer graphics, ranging from the textbook Principles of Digital Image Synthesis to the popular Graphics Gems series, offering practical algorithms for working programmers. Glassner has a PhD in Computer Science from UNC-Chapel Hill.

作者簡介(中文翻譯)

Andrew Glassner是一位專注於電腦圖學和深度學習的研究科學家。他目前在Weta Digital擔任高級研究科學家,致力於將深度學習與電影和電視視覺效果的製作相結合。他曾在IBM Watson實驗室、Xerox PARC和微軟研究院等實驗室工作。他曾擔任ACM TOG(圖學領域的頂級研究期刊)的主編,以及SIGGRAPH(圖學領域的頂級會議)的技術論文主席。他撰寫或編輯了十幾本關於電腦圖學的技術書籍,從教科書《數字圖像合成原理》到實用算法提供給工程師的《Graphics Gems》系列。Glassner擁有北卡羅來納大學教堂山分校的計算機科學博士學位。