New Racket Deep Learning Library

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Hi everyone,

I'd like to show off MIND, (GitHub - dev-null321/MIND: Deep learning library) which is a deep learning library I wrote in Racket. It can be used to make simple FNN systems. The library includes essential components such as tensor operations, activation functions, loss functions, and back-propagation for gradient calculation and weight updates. It's currently in it's infancy so it is lacking a lot of things that a great DL library would really need. So currently there is no support for a CNN system. Other things that need to be added are different kinds of activation functions, support for other DL architectures, optimizations, optimizers, etc.
I hope the community enjoys this, and that future improvements can be made.

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I'll be releasing a new update , with a test file for xor within the coming week. It will also include sigmoid, tahn, and a few other improvements. After this I plan to rewrite most of the tensor creation and matrix multiplication in C. The only hang ups are getting the tensor sizes to be correct and crushing all the bugs i'm occurring. More to come.

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Hi

Thank you for sharing your work. I can see a number of people have checked your repo

This is not a technology I am familiar with! I found Neuro-fuzzy - Wikipedia but would appreciate it if you know of a better primer?

What applications do you have planned for your library?

Best regards
Stephen :beetle: