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Nanthini's Projects

automate-move-files icon automate-move-files

A simple python program to move the files from Downloads folder into appropriate subfolder elsewhere. It's useful to declutter the Downloads folder and can help save a lot of time.

awesome-nlp icon awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

azureml-examples icon azureml-examples

Official community-driven Azure Machine Learning Examples, tested with GitHub Actions

cuml icon cuml

cuML - RAPIDS Machine Learning Library

gbm-bench icon gbm-bench

A benchmark to measure performance of popular Gradient boosting algorithms against popular ML datasets.

generalised-movie-recommendation icon generalised-movie-recommendation

A python program using Pandas to perform a generalised movie recommendation by merely loading the ml-100k dataset into Pandas and sorting them

integration icon integration

RAPIDS - combined conda package & integration tests for all of RAPIDS libraries

interpret icon interpret

Fit interpretable models. Explain blackbox machine learning.

interpret-community icon interpret-community

The Interpret Community extends Interpret repo with additional interpretability techniques and utility functions to handle real-world datasets and workflows.

k-means-on-yahoo-click-log icon k-means-on-yahoo-click-log

The project is mainly to demonstrate the performance in terms of convergence for Random Initialisation and K++ for K-Means Algorithm.

metaflow icon metaflow

Build and manage real-life data science projects with ease.

notebooks icon notebooks

Jupyter notebooks for the Natural Language Processing with Transformers book

optuna icon optuna

A hyperparameter optimization framework

pytext icon pytext

A natural language modeling framework based on PyTorch

raft icon raft

Rapids Analytics Framework Toolset to share building blocks between cuGraph and cuML

similar-duo icon similar-duo

Obtaining similar users in the ml-20m dataset using min hashing to obtain the signature matrix and LSH to obtain nearest neighbour.

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