Deep Learning - The Biggest Data Science Breakthrough of the Decade

The main difference between these newer networks (besides much improved performance) is that the algorithms can handle "deeper" networks better (more hidden units). If we're talking about Deep Belief Networks, they're not much like the old ANNs. DBNs are generative probabilistic graphical models using Bayesian inference.

Conceptually, going deeper (LOL) allows the networks to learn higher level concepts. For example, a 1 layer ANN (perceptron) can only learn linear functions, while a deep network is able to internally form a belief of what, say, a cat is.

More technically: Much of the work in ML is deciding what your inputs (features) should be. When classifying text documents, should you use word counts, bag of words, word stemming, character counts, etc. Should the model be linear, polynomial, gaussian, trigometric, etc. Deep learners try to automatically do feature selection and control the degrees of freedom in the model for you.

Also, deep learning is catching on in some industries. It has recently had huge successes in speech recognition, and all major companies developing this technology have started using it (e.g. Siri for one).

https://news.ycombinator.com/item?id=5376319

they use DNNs not DBNs (DBNs only used for pre-training, sometimes). Also if you read Microsoft's paper Table2, 7 hidden layer networks, which clearly qualifies as deep, work just fine with Back-propagation. Just a bigfat-MLP, no preprocessing! but 17.4 Word Error Rate (WER) vs 17.0 WER for DBN pre-training.

>they're not much like the old ANNs. DBNs are generative probabilistic graphical models using Bayesian inference.

MLPs (DNNs) can also be interpreted probabilistically. Just a directed model where inference is attained by marginalization of the hidden binary nodes in a layer-wise manner and by using a naive mean field approximation. All that to say the classic "forward-pass" ;).

Also could you indicate me a source confirming that Siri (Nuance) also switched to DNNs?. I am interested in that.

>Conceptually, going deeper (LOL) allows the networks to learn higher level concepts.

That is the really interesting part!. Now, I have not seen a proof for that. Wondering at individual neurons modeling individual features of e.g. a face or so is also a trend of the 90s and does not count as proof. I said this because it is what I usually hear.

Until now the justifications I saw for multiple layers of perceptrons being suitable for modeling arbitrary high level abstractions are reduced to

1) MLPs are universal approximators. This in my opinion is a superficial argument. GMMs also allow modeling "any" distribution and Taylor series any linear function, but in reality there are physical limitations to this argument. Maybe is true if you had a billion layer net, but will you get there?. If you had that computing power maybe a more realistic modeling of the brain might work better

2) They resemble how brain architecture works and similar arguments. Which I am fairly sure is not true. There are more human-brain based approaches to AI like e.g. cortical learning algorithms and those just seem to model that stuff to a certain extent.