Machine learning

Current implementation of Go binding can not specify options.

GPUOptions struct is in internal package. And go generate doesn't work for protobuf directory. So we can't specify GPUOptions for NewSession.

This issue is about the working group specially created for this task. If you are interested in helping out, take a look at this organization, or add me on Discord: ChainYo#3610

We are looking for contributing to HuggingFace's ONNX implementation for all available models on the HF's hub. There is already a lot of architectures implemented for converting PyTorch m

🚀 The feature, motivation and pitch

After the revert of pytorch/pytorch@7cf9b94 we've identified a need to add a lint that checks file names to ensure that they're compatible with Windows machines.

Observed error: (from example commit)

Error: error: invalid path 'test/test_ops_gradients.py '

A simple check on chang

Describe the issue linked to the documentation

Documentation should be changed to reflect that one-vs-rest is possible.

x = np.array([0,1,2,3,4,5,3,3,5,5,5,7,7,2])
x = x.reshape(-1,1)#this has 1 feature, therefore reshaping properly

y = [0,0,0,1,0,2,1,1,2,2,2,3,3,0] #note: y has multiple classes.


model = SVC(gamma = "auto", decision_function_shape="ovr")
model.fit(x,y)
p

Currently, this is defined only for arrays, but it seems like we should have

normalize(x::Number) = x / abs(x)

(I just came across this when trying to implement this algorithm for random unitary matrices, which is basically Q * Diagonal(normalize.(diag(R)), but I discovered that normalize didn't work.)

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.