Deep Learning: A Visual Approach (Paperback)
暫譯: 深度學習:視覺化方法 (平裝本)

Glassner, Andrew

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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)

這本豐富插圖、全彩的深度學習入門書,提供視覺和概念上的解釋,而非數學公式。您將學會如何使用關鍵的深度學習演算法,而無需複雜的數學知識。

自從電腦在棋藝上擊敗我們以來,它們在許多人的活動中變得越來越出色,從創作歌曲和生成新聞文章到幫助醫生提供醫療服務。

深度學習是這些突破的源頭,其驚人的能力在於能夠發現數據中隱藏的模式,使其成為人工智慧(AI)中增長最快的領域。我們手機上的數位助理使用深度學習來理解並智能地回應語音指令;汽車系統利用它安全地導航路面危險;在線平台使用它提供個性化的電影和書籍建議——可能性無窮無盡。

深度學習:視覺化方法適合任何想深入了解這個迷人領域的人,但不需要通常理解其內部運作所需的高級數學和程式設計。如果您想知道這些工具如何運作並親自使用它們,答案都在這些頁面中。而且,如果您準備好編寫自己的程式,隨附的Github儲存庫中還有許多補充的Python筆記本可以幫助您入門。

本書的對話風格、豐富的彩色插圖、啟發性的類比和真實世界的例子,巧妙地解釋了深度學習中的關鍵概念,包括:



- 文本生成器如何創作新穎的故事和文章

- 深度學習系統如何學會玩人類遊戲並獲勝

- 圖像分類系統如何識別照片中的物體或人

- 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AI核心的機器學習技術

各類知識探索者都可以利用深度學習:視覺化方法中涵蓋的強大理念,構建智能系統,幫助我們更好地理解這個世界及其所有居民。這是AI的未來,而這本書讓您能夠充分想像它。

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 是一位專注於計算機圖形學和深度學習的研究科學家。他目前是 Weta Digital 的高級研究科學家,負責將深度學習整合到世界級電影和電視視覺特效的製作中。他曾在 IBM Watson Lab、Xerox PARC 和 Microsoft Research 等實驗室擔任研究員。他曾擔任 ACM TOG 的主編,這是圖形學領域的頂尖研究期刊,以及 SIGGRAPH 的技術論文主席,SIGGRAPH 是圖形學領域的頂尖會議。他撰寫或編輯了十多本有關計算機圖形學的技術書籍,涵蓋從教科書 數位影像合成原理 到受歡迎的 Graphics Gems 系列,提供給在職程式設計師的實用演算法。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。