Deep learning made easy by Zygmunt Zając.
From the post:
As usual, there’s an interesting competition at Kaggle: The Black Box. It’s connected to ICML 2013 Workshop on Challenges in Representation Learning, held by the deep learning guys from Montreal.
There are a couple benchmarks for this competition and the best one is unusually hard to beat – only less than a fourth of those taking part managed to do so. We’re among them. Here’s how.
The key ingredient in our success is a recently developed secret Stanford technology for deep unsupervised learning, called sparse filtering. Actually, it’s not secret. It’s available at Github, and has one or two very appealling properties. Let us explain.
The main idea of deep unsupervised learning, as we understand it, is feature extraction. One of the most common applications are in multimedia. The reason for that is that multimedia tasks, for example object recognition, are easy for humans, but difficult for the computers*.
Geoff Hinton from Toronto talks about two ends of spectrum in machine learning: one is statistics and getting rid of noise, the other one – AI, or the things that humans are good at but computers are not. Deep learning proponents say that deep, that is, layered, architectures, are the way to solve AI kind of problems.
The idea might have something to do with an inspiration from how the brain works. Each layer is supposed to extract higher-level features, and these features are supposed to be more useful for the task at hand.
Rather say layered architectures are observed to mimic human results.
Just as a shovel mimics and exceeds a human hand for digging.
But you would not say operation of a shovel gives us insight into the operation of a human hand.
Or would you?