There are 27 repositories under hyperparameter-tuning topic.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Automated Machine Learning with scikit-learn
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
Sequential model-based optimization with a `scipy.optimize` interface
Time Series Forecasting Best Practices & Examples
Determined: Deep Learning Training Platform
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
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Sequential Model-based Algorithm Configuration
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
Tuning hyperparams fast with Hyperband
The world's cleanest AutoML framework ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other frameworks and cloud environments.
EvalML is an AutoML library written in python.
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
A web-based dashboard for Deep Learning
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
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.
Gradient based Hyperparameter Tuning library in PyTorch
Library for Semi-Automated Data Science
Sentence Classifications with Neural Networks
A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
An automatic ML model optimization tool.
State-of-the art Automated Machine Learning python library for Tabular Data
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
Audio feature extraction and classification
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
Continual Hyperparameter Selection Framework. Compares 11 state-of-the-art Lifelong Learning methods and 4 baselines. Official Codebase of "A continual learning survey: Defying forgetting in classification tasks." in IEEE TPAMI.
Parallel Hyperparameter Tuning in Python
Large scale and asynchronous Hyperparameter Optimization at your fingertip.
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Distribution transparent Machine Learning experiments on Apache Spark