CNN 101: Interactive Visual Learning for Convolutional Neural Networks
January 07, 2020 Β· Declared Dead Β· π CHI Extended Abstracts
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Authors
Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau
arXiv ID
2001.02004
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.LG
Citations
27
Venue
CHI Extended Abstracts
Last Checked
4 months ago
Abstract
The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep learning models. We present our ongoing work, CNN 101, an interactive visualization system for explaining and teaching convolutional neural networks. Through tightly integrated interactive views, CNN 101 offers both overview and detailed descriptions of how a model works. Built using modern web technologies, CNN 101 runs locally in users' web browsers without requiring specialized hardware, broadening the public's education access to modern deep learning techniques.
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