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Top 53 hyperparameter-tuning open source projects

Tune Sklearn
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
Adatune
Gradient based Hyperparameter Tuning library in PyTorch
Coursera Deep Learning Specialization
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
Sentence Classification
Sentence Classifications with Neural Networks
Rl Baselines3 Zoo
A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.
Milano
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
Pbt
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
Hyperopt Keras Cnn Cifar 100
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Mgo
Purely functional genetic algorithms for multi-objective optimisation
Mlprimitives
Primitives for machine learning and data science.
Rl Baselines Zoo
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Onepanel
The open and extensible integrated development environment (IDE) for computer vision with built-in modules for model building, automated labeling, data processing, model training, hyperparameter tuning and workflow orchestration.
Neuraxle
A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.
Sherpa
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
Deeplearning.ai Notes
These are my notes which I prepared during deep learning specialization taught by AI guru Andrew NG. I have used diagrams and code snippets from the code whenever needed but following The Honor Code.
Auto-Surprise
An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀
cerebro-system
Data System for Optimized Deep Learning Model Selection
diviner
Diviner is a serverless machine learning and hyper parameter tuning platform
map-floodwater-satellite-imagery
This repository focuses on training semantic segmentation models to predict the presence of floodwater for disaster prevention. Models were trained using SageMaker and Colab.
skrobot
skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
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