Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

(Deprecated) Scikit-learn integration package for Apache Spark

Python library to easily log experiments and parallelize hyperparameter search for neural networks

A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.

Hyperparameter tuning for machine learning models using a distributed genetic algorithm

Bayesian Optimization and Grid Search for xgboost/lightgbm

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

To design a predictive model using xgboost and voting ensembling techniques and extract insights from the data using pandas, seaborn and matplotlib

Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data

Machine learning toolkits with Python

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). This repository mirrors https://gitlab.com/NMOF/NMOF .

All codes, both created and optimized for best results from the SuperDataScience Course

Spark Parameter Optimization and Tuning

A PyTorch Based Deep Learning Quick Develop Framework. One-Stop for train/predict/server/demo

A library for creating and curating reproducible pipelines for scientific and industrial machine learning

Different hyperparameter optimization methods to get best performance for your Machine Learning Models

Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..

real-time grid search engine

Hyperparameters-Optimization