Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
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Updated
Sep 22, 2022 - Jupyter Notebook
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
To design a predictive model using xgboost and voting ensembling techniques and extract insights from the data using pandas, seaborn and matplotlib
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
All codes, both created and optimized for best results from the SuperDataScience Course
Learn to use Support Vector Machines in Python(sklearn) and R
Hyperparameter tuning for machine learning models using a distributed genetic algorithm
Udacity Machine Learning Course Predicting Boston Housing Prices
A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.
Different hyperparameter optimization methods to get best performance for your Machine Learning Models
Bayesian Optimization and Grid Search for xgboost/lightgbm
Machine learning toolkits with Python
Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..
Hyperparameter optimization algorithms for use in the MLJ machine learning framework
Tree based algorithm in machine learning including both theory and codes. Topics including from decision tree regression and classification to random forest tree and classification. Grid Search is also included.
Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
Spark Parameter Optimization and Tuning
A library for creating and curating reproducible pipelines for scientific and industrial machine learning
With some projects to develop "TOOLs" for better Modeling
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