hyper-parameter-optimization
There are 23 repositories under hyper-parameter-optimization topic.
D-X-Y/Awesome-AutoDL
Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)
open-edge-platform/training_extensions
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
THUMNLab/AutoGL
An autoML framework & toolkit for machine learning on graphs.
PKU-DAIR/open-box
Towards Generalized and Efficient Blackbox Optimization System/Package (KDD 2021 & JMLR 2024)
cansyl/DEEPScreen
DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images
THUMNLab/awesome-auto-graph-learning
A paper collection about automated graph learning
PKU-DAIR/mindware
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
timzatko/Sklearn-Nature-Inspired-Algorithms
Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.
sharmaroshan/Students-Performance-Analytics
Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades
cebes/hyper-optimizer
Convenient classes for optimizing Hyper-parameters, using Random search, Spearmint and SigOpt
SankethNagarajan/tracta_ml
Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms
e-baumer/pos
A gradient free optimization routine which combines Particle Swarm Optimization with a local optimization for each particle
machinedesign/grammaropt
Grammaropt : a framework for optimizing over domain specific languages (DSLs)
machinedesign/pipelineopt
Pipelineopt, sckit-learn automatic pipeline optimization
faprieto96/pipoh
Pipoh is a library that implements several diversification techniques base on mean-variance framework. In addition, it includes a novel purely data-driven methods for determining the optimal value of the hyper-parameters associated with each investment strategy.
fredericoschardong/ffnet-single-hidden-layer-hyper-parameterization
Python implementation that explores how different parameters impact a single hidden layer of a feed-forward neural network using gradient descent
HealthyData-Hub/Diagnosis-of-Breast-Cancer
To utilize the Breast Cancer Wisconsin Dataset for machine learning purposes. The aim is to diagnose breast cancer by employing a supervised binary, distance-based classifier (K Nearest Neighbours), which will classify cases as either benign or malignant.
Religious-downeaster605/student-grade-analysis
🎓 Analyze and predict student academic performance using socio-economic and behavioral factors with Python, enhancing educational decision-making.
rhysstubbs/HPOExperimentResults
Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)
teja0508/Students-Performance-Analytics
Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades