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by medo-bear
1426 days ago
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> his means cl pagckages can be "done". this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university and it has a convolutional layer implemented https://github.com/marcoheisig/Petalisp https://github.com/marcoheisig/Petalisp/blob/master/examples... |
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