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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Currently code like this is repeated several times:
field = mapping.STORAGE_TENSOR_TYPE_TO_FIELD[
mapping.TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE[data_type]]
getattr(tensor, field)This is repetitive and can be encapsulated in a helper function.
Also the name "storage tensor type" is misleading and has led to at least one bug.
We should:
- Add a function that does all this
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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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Typo under the description: Returns a containing. Returns a what?
Document Details
- ID: d2dc315d-96d7-e54f-6e90-fec6ed09481c
- Version Independent ID: ab5d0a68-35d6-ef5f-786e-d89e7fee8034
- Content: [DataFrameColumn.Info Method (Microsoft.Data.Analysis)](https://docs.microsoft.com/e
Discussed in mindsdb/mindsdb#2214
Originally posted by blaszta May 13, 2022
Is there any plan to integrate MindsDB with Metabase (an open source BI)?
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Is your feature request related to a problem? Please describe.
In time series plotting module, lot of plots are customized at the end - template, fig size, etc. Since the same code is repeated in all these plots, maybe this could be modularized and reused.
with fig.batch_update():
template = _resolve_dict_keys(
dict_=fig_kwargs, key="template", defaults=fig_defaultWe currently have read and write capabilities but do not support deleting. We could add a few calls like delete delete_all and some recursive way of deleting.
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🚨 🚨 Feature Request
- A new implementation (Improvement, Extension)
Is your feature request related to a problem?
Currently, if a user tries to access an index that is larger than the dataset length or tensor length, an internal error is thrown which is not easy to understand.
Description of the possible solution
We can catch the error and throw a more descriptive e
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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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你好,请问怎么装载 ONNX 模型,目前只看到 Oneflow->ONNX 工具,没有找到 ONNX->Oneflow 工具。
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?
Is your feature request related to a problem? Please describe.
The current Feast online store for GCP implementation requires Firestore in Datastore mode. Firestore can only be in one mode at a time per GCP account. You cannot use native mode for some applications and Datastore mode for others within the same account. Adding a feature store to an existing GCP account that uses native mode wou
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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.