THE VIDEO SHELF · unsupervised feature learning
Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning
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.
Why HN readers recommended it
HN commenters recommended it for years as a simple, exciting overview of the deep learning wave.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- Google TechTalks
- Topic
- unsupervised feature learning
- Format
- talk
- Level
- intermediate
- HN recommenders in dataset
- 9
- HN mentions in dataset
- 11
- Recommendation years in dataset
- 2011–2013
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
A good intro page with lots of links: [LINK] A lot of the newest work is under a few different names: "deep learning", "convolutional deep networks", "unsupervised feature learning", etc. Here's a great talk by Andrew Ng of Stanford, who's a recent convert to this …
apu on Hacker News · 2011-05
In Neural Networks making a come-back?This seems very related to this [VIDEO] speak by Andrew Ng. It is a 40min speak, but he explains very simply how all this works for images and some examples about the audio case. It is incredible how using this deep learning techniques we can …
brutuscat on Hacker News · 2012-06
In Microsoft Research make breakthrough in audio speech recognitionCheck out this (awesome) talk: [VIDEO] There are a number of areas in which unsupervised feature learning outperforms the hand-programmed models we have been fine-tuning for decades.
kevin1024 on Hacker News · 2012-06
In Google & Stanford create a digital brain that learns to identify a human face
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.