Repository Index

Provide an input CSV and a target field to predict, generate a model + code to run it.

• python
• tensorflow
• keras
• xgboost

AI on the way. An auto deep learning pipe dream. An RDBMS approach to deep learning. Declarative, explainable, scalable, optimizable, easy to deploy, all that good stuff.

• pytorch
• lazy-evaluation
• python
• compiler

Python AutoML for Trading Systems and Sports Betting

• machine-learning
• predictive-analytics
• classification
• regression

WARNING: Katib project is DEPRECATED, users should migrate to OptimizationJob and Kubeflow Trainer

• ai
• automl
• huggingface
• hyperparameter-tuning

OpenMMLab Model Compression Toolbox and Benchmark.

• nas
• pruning
• knowledge-distillation
• spos

Machine Learning in R

• machine-learning
• data-science
• tuning
• cran

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

• vizier
• hyperparameter-optimization
• tuning
• tuning-parameters

[UNMAINTAINED] Automated machine learning for analytics & production

• machine-learning
• data-science
• automated-machine-learning
• gradient-boosting

Hyperparameter Experiments with TensorFlow and Keras

• deep-learning
• keras
• keras-tensorflow
• hyperparameter-optimization

AIDE: an LLM agent for machine learning engineering - the research Weco grew out of. Referenced in OpenAI MLE-bench.

• ai
• data-science
• llm
• machine-learning

Official Implementation of 'Fast AutoAugment' in PyTorch.

• deep-learning
• convolutional-neural-networks
• pytorch
• augmentation

MLBox is a powerful Automated Machine Learning python library.

• machine-learning
• auto-ml
• kaggle
• deep-learning

The world's cleanest AutoML library ✨ - 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 libraries, frameworks and MLOps environments.

• pipeline
• pipeline-framework
• machine-learning
• deep-learning

optimization routines for hyperparameter tuning

Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.

• python
• scikit-learn
• machine-learning
• artificial-intelligence

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

• svm
• regression
• classification
• machine-learning