Fast and Accurate ML in 3 Lines of Code
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Nov 11, 2026 - Python
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Fast and Accurate ML in 3 Lines of Code
python data-science machine-learning natural-language-processing computer-vision deep-learning time-series scikit-learn tabular-data pytorch forecasting hyperparameter-optimization ensemble-learning object-detection transfer-learning structured-data gluon automl automated-machine-learning autogluon
Using data analytics and machine learning to create a comprehensive and profitable system for predicting the outcomes of NBA games.
Traffic analysis for Tor-based malware detection and classification
Train and deploy ML models in the cloud
python aws machine-learning cloud time-series tabular-data regression forecasting classification structured-data sagemaker autogluon
TSForecasting: Automated Time Series Forecasting Framework
python data-science machine-learning timeseries scikit-learn xgboost forecasting lightgbm automl timeseries-analysis catboost timeseries-forecasting autogluon automl-pipeline automated-forecasting automated-timeseries-forecasting
Corporate Credit Rating Prediction with AWS SageMaker JumpStart
Deploy automl models for tabular tasks on AWS Sagemaker with AutoGluon
Computational experiments for the paper "A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost" (IJCNN 2021)
Deploy AutoML models for image classification on AWS Sagemaker with AutoGluon
Benchmark for some usual automated machine learning, such as: AutoSklearn, MLJAR, H2O, TPOT and AutoGluon. All visualized via a Dash Web Application
python benchmark machine-learning deep-learning kaggle dataset dash openml tpot auto-sklearn ploty mljar h2o-automl autogluon
This repository shows how to use AWS step functions to train and deploy Autogluon tabular models on Amazon SageMaker
h2o-automl rapids autokeras autogluon lazypredict autots evalml autonlp autoviml sklearn-automl tpot-automl
AI capability development platform using AutoML and AutoGluon
This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.
python data-science machine-learning deep-learning exploratory-data-analysis machine-learning-algorithms jupyter-notebook hyperparameter-optimization amazon-web-services boosting-algorithms feature-engineering model-evaluation automl stacking model-building ensemble-methods autogluon
使用比赛方提供的脱敏数据,进行客户信贷流失预测。
machine-learning eda lightgbm ann xgboost-model stratified-sampling tabnet stacking-ensemble autogluon stratified-cross-validation smote-sampling smote-oversampler stacking-classifier
In this project, you'll use the AutoGluon library to train several models for the Bike Sharing Demand competition in Kaggle. You will be using Tabular Prediction to fit data from CSV files provided by the competition.
Developers of all skill levels get hands on with machine learning through a cloud based 3D racing simulator, fully autonomous 1/18th scale race car driven by reinforcement learning, and global racing league.
aws machine-learning reinforcement-learning mxnet scikit-learn pytorch image-classifier vggnet udacity-nanodegree proximal-policy-optimization ppo dog-breed-classifier aws-deepracer ai-programming-with-python autogluon bike-share-demand-prediction
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