Google TechTalks
9 HN recommenders · recommended in 7 months, 2011–2013
48:10 · 123K views · Apr 11, 2011
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.
HN commenters recommended it for years as a simple, exciting overview of the deep learning wave.
11 comments from Hacker News
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 area: this video (Note that this is quite a one-sided view of things, but it does convey the excitement of deep-learning researchers and the potential of what might be possible.) Although I'm not in deep learning myself (I'm a computer vision researcher), here's a TL;DR as I understand it: rather than having people in specific domains such as computer vision or speech processing create their own features, the idea is to take raw inputs (pixels in the case of images) and train multi-layer neural net architectures that "learn" the relevant higher-level features in an unsupervised way (i.e., without labeled training data). Some of these seem to be pulling out interesting features and perform competitively on a few benchmarks in vision and other fields. I'm not sold on this yet, because it seems like the complexity of designing features has merely been traded in for the complexity of designing different learning architectures, but it's certainly becoming quite popular these days (mostly led by Geoff Hinton of Toronto, Yoshua Bengio of Montreal, and Yann LeCunn of NYU).
This seems very related to this 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 teach this "neural networks" to recognize such complicated patterns. It is like reverse engineering the brain's algorithms. BTW I took his Coursera's course about Machine Learning and it was great! I also recommend it A LOT to gather basic ML knowledge.
Andrew Ng has a fantastic example of visualizing deep belief nets: this 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).
Check out this (awesome) talk: this video There are a number of areas in which unsupervised feature learning outperforms the hand-programmed models we have been fine-tuning for decades.
the one overview I've found the most useful is this video (Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning by Andrew Ng in April 2011).