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Big Data Big Design (Why Designers Should Care about Artificial Intelligence)
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Product Details
Author:
Helen Armstrong, Keetra Dean Dixon
Format:
Paperback
Pages:
176
Publisher:
Chronicle Books (October 19, 2021)
Language:
English
ISBN-13:
9781616899158
ISBN-10:
1616899158
Weight:
16.16oz
Dimensions:
7" x 8.45" x 0.6"
File:
hbgusa-hbgusa_Chronicle_onix30_P10160022_06022026_Complete-20260602.xml
Folder:
hbgusa
List Price:
$29.95
Case Pack:
28
As low as:
$23.06
Publisher Identifier:
P-CHRONICLE
Discount Code:
B
Audience:
General/trade
Country of Origin:
China
Pub Discount:
65
Imprint:
Princeton Architectural Press
Overview
Big Data, Big Design provides designers with the tools they need to harness the potential of machine learning and put it to use for good through thoughtful, human-centered, intentional design.
Enter the world of Machine Learning (ML) and Artificial Intelligence (AI) through a design lens in this thoughtful handbook of practical skills, technical knowledge, interviews, essays, and theory, written specifically for designers. Gain an understanding of the design opportunities and design biases that arise when using predictive algorithms. Learn how to place design principles and cultural context at the heart of AI and ML through real-life case studies and examples. This portable, accessible guide will give beginners and more advanced AI and ML users the confidence to make reasoned, thoughtful decisions when implementing ML design solutions.
Enter the world of Machine Learning (ML) and Artificial Intelligence (AI) through a design lens in this thoughtful handbook of practical skills, technical knowledge, interviews, essays, and theory, written specifically for designers. Gain an understanding of the design opportunities and design biases that arise when using predictive algorithms. Learn how to place design principles and cultural context at the heart of AI and ML through real-life case studies and examples. This portable, accessible guide will give beginners and more advanced AI and ML users the confidence to make reasoned, thoughtful decisions when implementing ML design solutions.
