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Deep Learning with Wolfram Neural Networks?

Posted 1 year ago
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Is anyone interested in and actively using Wolfram to do Deep Learning research?

I am doing research in all areas of Deep Learning with my focus currently on VGG style of convolution neural networks (CNN). I will be branching out very soon to RLN, RNN, and GAN research very soon.

I would like to be able to compare experiences and lessons learned using Wolfram in these areas.

Thanks for any interest in discussions on Deep Learning.

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Just a note that while CNNs were already present in Wolfram Language 11.0:

https://www.wolfram.com/language/11/neural-networks

RNNs were added in Wolfram Language 11.1 and examples can be found in docs in:

http://reference.wolfram.com/language/ref/NetTrain.html

NetTrain >> Applications >> Sequence Learning and NLP

Please also see full info for available layers here:

http://reference.wolfram.com/language/guide/NeuralNetworks.html

Thank Vitaliy for the information :)

I have doing numerous VGG CNN instances with 11.1 with success. Much to learn about the possibilities.

Do you know of any good examples with Wolfram Mathematica doing:

  1. RLN
  2. GAN Deep learning cases?

I've never seen the acronym "RLN". Could you clarify what you mean by this?

Hi Sebastian,

Sorry if I miss spoke, I mean Reinforcement Learning Neural Network (RLNN).

The basic types we are working on are:

  1. CNN - Convolution NN
  2. Reinforcement Learning Neural Network (RLNN)
  3. RNN - Recurrent NN
  4. GAN - Generative Adversarial Networks

To date I have been working on variation of the VGG implementation of CNNs.

The other type I will be working on shortly, and at this point getting up to speed.

One further note, I am using Tensor Flow on also on a local GPU cube server, and on p2 blade instances at AWS.

This is still not entirely clarified, as "RLNN" is still a non-standard acronym (as far as I know). Are you referring to policy networks in reinforcement learning? Perhaps send me a link to a paper or page which does what you want.

I am not an expert in RL. The paper of interest is: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

Sebastian -> I am thoroughly enjoying using Mathematica 11.1 to explore and learn on DL technology, thanks for your team's excellent work.

Vitaliy,

Is the neural net code in Mathematica all "home grown" or do you use software such as tensorflow under the hood?

Regards

We use Apache MXNet (http://mxnet.io/) as a backend, and we also contribute improvements to this open source project.

Sebastian,

So the documentation of MXnet.io should be helpful for using DL in Mathematica 11.1?

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