Machine learning

Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.

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.

First good issue

A current error is that a user forwards a batched tensor of input_ids that include a padding token, e.g. input_ids = torch.tensor([["hello", "this", "is", "a", "long", "string"], ["hello", "<pad>", "<pad>", "<pad>", "<pad>"]]

In this case, the attention_mask should be provided as well. Otherwise the output hidden_states will be incorrectly computed. This is

🚀 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

See in #22547

MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes

We need to rep

I suggest adding Pluto.jl to the browser-based workflow: https://docs.julialang.org/en/v1/manual/workflow-tips/#Browser-based-workflow

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.

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Machine learning

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tensorflow / tensorflow

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First good issue

pytorch / pytorch

🚀 The feature, motivation and pitch

keras-team / keras

scikit-learn / scikit-learn

tesseract-ocr / tesseract

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fengdu78 / Coursera-ML-AndrewNg-Notes

ageron / handson-ml

CMU-Perceptual-Computing-Lab / openpose

terryum / awesome-deep-learning-papers

ultralytics / yolov5

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