92 skills found · Page 2 of 4
keplr-io / PicardEasily declare large spaces of (keras) neural networks and run (hyperopt) optimization experiments on them.
ml-tooling / Lazycluster🎛 Distributed machine learning made simple.
hyperopt / Hyperopt NnetHyperparameter optimization for neural networks
devbootstrap / Freqtrade Hyperopt Running In Cloud ExampleAn example of optimizing an existing strategy using Freqtrade Hyperopt Parameters
RasaHQ / Nlu HyperoptFind the best hyperparameters for your Rasa NLU model
Mavenoid / Prompt HyperoptImprove prompts for e.g. GPT3 and GPT-J using templates and hyperparameter optimization.
bits-bytes-nn / Mofc Demand ForecastTime Series Forecasting for the M5 Competition
TheodoreWolf / HyperoptaxParallel hyperparameter tuning with JAX
DutchCryptoDad / FreqtradeHyperoptGuideRepository that belongs to my hyperparameter optimisation video on Youtube
28dec / E0V1E HOThis is my personal hyperopt version of E0V1E strategy
zygmuntz / Kaggle Burn CpuCode for the "Burn CPU, burn" competition at Kaggle. Uses Extreme Learning Machines and hyperopt.
jakob-r / MlrHyperoptEasy Hyper Parameter Optimization with mlr and mlrMBO.
michaelrzhang / LLM HyperOptUsing Large Language Models for Hyperparameter Optimization
PhilipMay / MltbMachine Learning Tool Box
ISG-Siegen / Auto SurpriseAn AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning :rocket:
nextflow-io / HyperoptNextflow pipeline for hyperparameter optimization of machine learning models
hyperopt / Hyperopt GpsmboGaussian Process optimization algorithm for Hyperopt
yuvalatzmon / SACRED HYPEROPT ExampleA minimal example for integrating a general machine learning training script with SACRED experimental framework, and HyperOpt (Distributed Asynchronous Hyperparameter Optimization).
nphdang / Bandit BOBayesian Optimization for Categorical and Continuous Inputs
abhishekdbihani / Home Credit Default Risk RecognitionThe project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.
