ml
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.
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New Operator
Describe the operator
Why is this operator necessary? What does it accomplish?
This is a frequently used operator in tensorflow/keras
Can this operator be constructed using existing onnx operators?
If so, why not add it as a function?
I don't know.
Is this operator used by any model currently? Which one?
Are you willing to contribute it?
MLflow Roadmap Item
This is an MLflow Roadmap item that has been prioritized by the MLflow maintainers. We're seeking help with the implementation of roadmap items tagged with the help wanted label.
For requirements clarifications and implementation questions, or to request a PR review, please tag @BenWilson2 in your communications related to this issue.
Proposal Summary
Includ
Every kubeflow image should be scanned for security vulnerabilities.
It would be great to have a periodic security report.
Each of these images with vulnerability should be patched and updated.
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Let's make the error message more actionable.
I would recommend adding similar named column(s):
- $"Provided {columnPurpose} column '{columnName}' not found in training data."
+ $"Provided {columnPurpose} column '{columnName}' not found in training -
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Metaflow currently supports Py>=3.4 (with limited support for Py2.7) and R>=3.8. The GH tests only test for Py3.7 and R4.1. We should ensure we test on the whole set of permutations.
Is your feature request related to a problem? Please describe.
The current value of alpha value is hardcoded in many places to 0.05.
Describe the solution you'd like
Take this as a setup argument and use it everywhere for consistency. The default value can be 0.05.
We need to increase the number of datasources that MindsDB supports. This task should add a new datasource for connecting to Redash. You can get more info on Redash docs.
If possible, please include a test for the datasource. You can check the example here.
Note: if you are familiar with another datasource tha
🚨 🚨 Feature Request
If your feature will improve HUB
To explore the structure of a dataset it is convenient to have nicer and more informative prints of dataset objects and samples
Description of the possible solution
1) show ds
now
> ds
Dataset(path='hub://activeloop/abalone_full_dataset', tensors=['length', 'diameter', 'height', 'weight'])-
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In Ue format string it represent float with comma separator, it crash css style
To fix it you can Round/replace/incluse culture info
samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentiment.Client/Shared/HappinessScale.razor
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I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?
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交叉熵损失 API 设计
在oneflow里,交叉熵损失有以下几种:
- binary_cross_entropy_loss
- binary_cross_entropy_with_logits_loss
- sparse_cross_entropy
- distributed_sparse_cross_entropy
- cross_entropy
- sparse_softmax_cross_entropy
- softmax_cross_entropy
在pytorch里,交叉熵损失有以下几种:
- binary_cross_entropy
- binary_cross_entropy_with_logits
- cross_entropy
由此可见,oneflow中交叉熵损失存在API冗余,重复,容易让用户疑惑,因此,这里应该精简一下。除此之外,label smooth
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- Wikipedia
- Wikipedia


Current implementation of Go binding can not specify options.
GPUOptions struct is in internal package. And
go generatedoesn't work for protobuf directory. So we can't specify GPUOptions forNewSession.