HackerTube.dev

Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning

Google TechTalks · 122.9K views · 2011-04-11 · 48:10

Open on YouTube ↗

If playback is restricted in your region, try opening the original video. Use Mark watched to add it to your marked videos.

Overview

Andrew Ng presents unsupervised feature learning and deep learning, showing how algorithms can learn representations from unlabeled data instead of hand-designed features. He gives examples in vision and audio, including visualizations of features learned by successive layers.

On Hacker News

HN commenters recommended it for years as a simple, exciting overview of the deep learning wave.

Recommended by 9 HN people across 7 months · HN mentions 2011–2013

Topics

Machine learning & deep learningComputer vision, speech & NLP
Search by tags
deep learningandrew ngneural networksfeature learningcomputer vision

What people said

michael_nielsen · 2013-04 · Strong recommendation

Andrew Ng has a fantastic example of visualizing deep belief nets: [VIDEO] The example starts at about 18:20. You really need to have watched the previous material to understand it, but the basic idea is that he's plotting the hierarchy of features learned by successive layers of a deep neural net (a sparse autoencoder, IIRC, it's been a while since I watched it).

Read the original HN comment ↗
pilooch · 2012-11 · Strong recommendation

the one overview I've found the most useful is [VIDEO] (Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning by Andrew Ng in April 2011).

Read the original HN comment ↗
FrojoS · 2012-08 · Strong recommendation

Thanks for pointing this out. Here is a similar awesome by Andrew Ng [VIDEO] who teaches the original Coursea Machine Learning class.

Read the original HN comment ↗

Sources

Video by Google TechTalks, played through YouTube.

Data and selection notes

HN summaries and labels may contain AI errors. HN dates refer to discussions, not video publication.

HN Library: HackerTube export (2026-10-31) and open-index/hacker-news. Upstream summaries and classifications may contain AI errors.

Views, duration and playback status refresh separately from the published catalog. When an update fails, existing data remains visible.

Selection checks
  • SaaS & AI candidates: Topic keywords do not match
  • Editor-approved picks: Editorial review is required

Rule matches identify candidates. They do not guarantee video quality or financial results.

About the library and its sources