52 Open source projects that are alternatives of or similar to diviner

Machine Learning Tool Box

Iterated Racing for Automatic Algorithm Configuration

Distribution transparent Machine Learning experiments on Apache Spark

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

This repository focuses on training semantic segmentation models to predict the presence of floodwater for disaster prevention. Models were trained using SageMaker and Colab.

A general-purpose pythonic genetic algorithm.

Audio feature extraction and classification

Parallel Hyperparameter Tuning in Python

skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

Hyperparameter optimization package of the mlr3 ecosystem

Generalized and Efficient Blackbox Optimization System.

A scikit-learn compatible implementation of hyperband

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

Gradient based Hyperparameter Tuning library in PyTorch

Library for Semi-Automated Data Science

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 Classifications with Neural Networks

An automatic ML model optimization tool.

A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.

Time Series Forecasting Best Practices & Examples

EvalML is an AutoML library written in python.

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Population Based Training (in PyTorch with sqlite3). Status: Unsupported

State-of-the art Automated Machine Learning python library for Tabular Data

AutoML frAmework for Neural Networks

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

Determined: Deep Learning Training Platform

Purely functional genetic algorithms for multi-objective optimisation

Primitives for machine learning and data science.

An Interactive Approach to Understanding Deep Learning with Keras

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

Automated Machine Learning with scikit-learn

Sequential Model-based Algorithm Configuration

Tuning hyperparams fast with Hyperband

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

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.

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.

A web-based dashboard for Deep Learning

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

Human-explainable AI.

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.

Sequential model-based optimization with a `scipy.optimize` interface

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

A library for performing hyperparameter optimization

R interface to Keras Tuner

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀

Bayesian Optimization and Grid Search for xgboost/lightgbm

Black box hyperparameter optimization made easy.

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Data System for Optimized Deep Learning Model Selection