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.
Here are 2,548 public repositories matching this topic...
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Aug 21, 2020 - Jupyter Notebook
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Sep 20, 2020 - JavaScript
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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Sep 2, 2020 - Jupyter Notebook
Bug Report
These tests were run on s390x. s390x is big-endian architecture.
Failure log for helper_test.py
________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________
self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>
def test_make_tensor(self): # type: () -> None
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Nov 21, 2018 - Shell
MLflow seems to have a length limit of 5000 when setting tags (see below).
[...]
File "/home/smay/miniconda3/envs/py38/lib/python3.8/site-packages/mlflow/utils/validation.py", line 136, in _validate_length_limit
raise MlflowException(
mlflow.exceptions.MlflowException: Tag value '[0.8562690322984875, 0.8544098885636596, 0.8544098885636596, 0.8544098885636596, 0.85440988856365There are 2 places we are using BufferBlock<T> today:
We should consider replacing this depende
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Dec 17, 2019 - Python
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Sep 20, 2020 - C++
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Sep 18, 2020 - C++
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Sep 15, 2020 - Python
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Feb 8, 2020 - Python
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Sep 20, 2020 - Python
All available samples code target .Net Core, Do we have samples for .Net Framework ?
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Sep 20, 2020 - Python
When setting fix_imbalance=True,fix_imbalance_method=imblearn.over_sampling.RandomOverSampler in setup (or any other imblearn method, including SMOTE), I always get this error when calling create_model('rf'):
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-164-5b7c9
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?
I am aggregating some events for 7 days from a partitioned table, and want to use mockTable for integration tests of my SQL.
SELECT flight_id AS flightId, campaign_id AS campaignId, ad_format FROM ad-selfserve.ad_platform_services.clean_flights_* WHERE _TABLE_SUFFIX >= $START_DATE AND _TABLE_SUFFIX < $END_DATE)
tests:
`
val CLEAN_FLIGHTS_TABLE_REF = "ad-selfserve:ad
Problem
Since Java 8 was introduced there is no need to use Joda as it has been replaced the native Date-Time API.
Solution
Ideally greping and replacing the text should work (mostly)
Additional context
Need to check if de/serializing will still work.
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Sep 20, 2020 - C++
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Sep 20, 2020
Is your feature request related to a problem? Please describe.
We have a mechanism to capture logs in production that doesn't require log files collection. However, there is no option to disable local log files generation.
Describe the solution you'd like
A configuration option for user to disable logging to files.
Describe alternatives you've considered
Accept the default beh
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Aug 8, 2020 - Ruby
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Jun 13, 2020 - Erlang
Tidy empty notebooks
There are a number of empty notebooks. Task here is to find them all, and convert each one to its own backlog ticket. Delete those notebooks and remove references to them from other notebooks. You'll also need to check if any of the text surrounding links needs adjusting.
- Wikipedia
- Wikipedia
Please make sure that this is a bug. As per our
GitHub Policy,
we only address code/doc bugs, performance issues, feature requests and
build/installation issues on GitHub. tag:bug_template
System information
example script provided in TensorFlow): Yes