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There's also Ersatz (http://www.ersatz1.com) -- deep learning as a service, provides a web interface that allows you to train different neural net architectures and then run them via an API, the networks are GPU backed, been in beta since January. I'm the founder and at NIPS now if anyone wants to ask me about it in person.

I was wondering when you were going to pop up here. Ersatz has a lot of potential, but as it stands now it's more or less a web UI for ensembling the pre-implemented models (correct me if I'm wrong). So if you don't want an autoencoder, convnet or a RNN, you're sort of out of luck, no?

I know the original website advertised 'custom architectures', but it's not entirely clear to me (... not that it necessarily should be) what the route for Ersatz's current implementation to something like that is. Comments?

haha, I guess I do have a way of popping up anytime somebody's talking about deep learning on the internet...

But yeah, fair points re: ersatz. We've got RNNs, autoencoders, conv nets, and deep feed forward nets w/ dropout, different types of nonlinearities, etc etc. I think these represent a pretty flexible set of architectures--but you're right, if you're looking for an RBM, you're out of luck for now. From there, it's a web interface and API that make it pretty straightforward to get started with these types of architectures. Which is still pretty damned cool, if I do say so myself...

I think of it like this:

* Use theano if you want maximum flexibility (and maximum difficulty in getting to results)

* Use pylearn2 if you want a really fair amount of flexibility and pre-built implementations of neural networks. It is, however, difficult to get started with. Otherwise it's awesome.

* Use Ersatz if you want to use neural networks without knowing how to build them--but also know that you're giving up some flexibility and Ersatz is a bit opinionated--which, honestly, i'm not convinced is a bad thing for the type of market we're trying to target (non-ML researchers, really)

Very different offerings for different needs.

Re: custom architectures, yeah, you're right--bottom line is allocation of resources--what should our team spend time on? Because we're bootstrapped, the answer to that is whatever people are asking for (and--pretty importantly--willing to pay for). So far, lack of model types hasn't been a deal breaker for us so we've been spending time improving the API, getting it to run faster, deal better with larger and larger amounts of data, etc. etc. etc. I do have some ideas on how "custom architectures" could work, but we're focusing on polishing the current offering for now.

So yes, I agree, Ersatz is not yet living up to its full potential. But that will come, one step at a time. If theano and pylearn2 seem too complicated, try Ersatz, it's getting better every day.

This is awesome and I'm sure it will be a boon to app developers who want to include Machine Learning capabilities in their apps.

It looks to me as though Ersatz's focus is on providing a limited range of relatively standard models, but make them highly accessible, stable, fast, and suitable for production, whereas most available frameworks like Theano, PyLearn2, etc are more geared to the tinkering researchers and less to be used in actual products.

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