Popular GitHub Repositories for Optuna

? The experiment tracker for foundation model training

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.

QSARtuna: QSAR model building with the optuna framework

Make GNN easy to start with

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

GANs for Time series analysis (Synthetic data generation, anomaly detection and interpolation), Hypertuning using Optuna, MLFlow and Databricks

This is the implementation of the paper Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions

PyTorch-Lightning Library for Neural News Recommendation

The Optuna MCP Server is a Model Context Protocol (MCP) server to interact with Optuna APIs.

Going beyond BEDMAP2 using a super resolution deep neural network. Also a convenient flat file data repository for high resolution bed elevation datasets around Antarctica.

This is the implementation of the paper Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions

This repository contains code for estimating the State of Charge (SoC) of LG HG2 batteries using Fully Connected Network (FCN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models along with optuna based hyperparameter tuning.

PyTorch tutorial for using RNN and Encoder-Decoder RNN for time series forecasting

LightGBM + Optuna: Auto train LightGBM directly from CSV files, Auto tune them using Optuna, Auto serve best model using FastAPI. Inspired by Abhishek Thakur's AutoXGB.

认识一下 Lumina——我的专属聊天机器人助手，能解答你的任何问题。Lumina 借助 Optuna、RAG、LangChain 和 Pinecone 向量数据库的力量，提供友好支持和针对所有对话的智能解决方案。它是北卡罗来纳大学 COMP-488 课程中期项目的一部分。

?? AllenNLP plugin for adding subcommands to use Optuna, making hyperparameter optimization easy

Train, evaluate, and optimize implicit feedback-based recommender systems.

Tutorial on training a PyTorch neural network model using MLflow for experiment tracking & Optuna for hyperparameter optimization.

Classifying Audio to Emotion