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machine-learning-algorithms

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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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nni

microsoft / nni

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Learn-Data-Science-For-Free

therealsreehari / Learn-Data-Science-For-Free

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This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in Twitter.

data-science machine-learning natural-language-processing computer-vision machine-learning-algorithms artificial-intelligence neural-networks deeplearning datascienceproject
  • Updated Apr 2, 2021

rapidsai / cudf

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rapidsai / cuml

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[DOC] Add docstrings to functions, classes, and methods in api_context_managers.py

wphicks
wphicks commented Feb 8, 2021

Report needed documentation

Report needed documentation
While the estimator guide offers a great breakdown of how to use many of the tools in api_context_managers.py, it would be helpful to have information right in the docstring during development to more easily understand what is actually going on in each of the provided functions/classes/methods. This is particularly important for

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good first issue ? - Needs Triage doc
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cleanlab / cleanlab

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The standard package for machine learning with noisy labels and finding mislabeled data. Works with most datasets and models.

machine-learning machine-learning-algorithms weak-supervision semi-supervised-learning unsupervised-learning data-cleaning clean-data learning-with-confident-examples noisy-data data-centric confident-learning latent-estimation robust-machine-learning learning-with-noisy-labels
  • Updated Jun 6, 2021
  • Python

stellargraph / stellargraph

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Consolidate example graphs in unit tests

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adocherty
adocherty commented Nov 27, 2019

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

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good first issue sg-library

krishnakumarsekar / awesome-quantum-machine-learning

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Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

machine-learning awesome ai qml tensorflow quantum machine-learning-algorithms artificial-intelligence quantum-computing artificial-neural-networks awesome-list fcm kmeans ant-colony-optimization quantum-programming-language hmm-model qubits knn-classification quantum-ai
  • Updated Oct 26, 2020
  • HTML

tslearn-team / tslearn

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KMeans question

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ssimontacchi
ssimontacchi commented Jun 20, 2020

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually

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bug good first issue
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