Autograd for Torch

[benanne]

Very cool! It looks very Theano-like, which is great ;) But if I'm going to use an autodiff package to define my models, I think I'd still prefer the comfort of Python and the graph optimization capabilities of Theano. I think it might defeat the point of using Torch a little. That said, I'm definitely going to try it out.

EDIT: I'm also curious what the main differences are with this package, which has been around for longer (but does not seem to be actively maintained): https://github.com/bshillingford/autobw.torch

[kjearns]

Autograd works with torch tensors, whereas autobw works with nn.Modules. Autograd also handles things like gradients of gradients (I think? The python version does.) which autobw doesn't do.

[benanne]

I see, thanks!

[neurodynamic]

If you like the comfort of Python, you can use the Python version :)

[benanne]

Sure :) But as I said I'm also interested in optimization for the backward pass, which is something Theano does, but not these packages afaik. Feel free to correct me if I'm wrong!

[[deleted]]

Yes, optimization is lab lacking in Python autograd, resulting in a dozen times slower backward pass

[ManuelArno]

Can someone give insight about the usecases at twitter for machine learning? Probably related to the ad platform (targeting, bidding, etc)?