Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Methodology
hyper-parameter tuning, grid search bayesian optimization evolutionary algorithms genetic programming cross validation k-fold Neural Architecture Search with Reinforcement Learning
Libraries
Optuna
auto-sklearn
Ludwig
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.
turicreate
Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
- Easy-to-use: Focus on tasks instead of algorithms
- Visual: Built-in, streaming visualizations to explore your data
- Flexible: Supports text, images, audio, video and sensor data
- Fast and Scalable: Work with large datasets on a single machine
- Ready To Deploy: Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps
PyCaret
PyCaret is an open source low-code machine learning library in Python
that aims to reduce the hypothesis to insights cycle time in a ML experiment.
It enables data scientists to perform end-to-end experiments quickly and efficiently.
In comparison with the other open source machine learning libraries,
PyCaret is an alternate low-code library
that can be used to perform complex machine learning tasks with only few lines of code.
PyCaret is essentially a Python wrapper
around several machine learning libraries and frameworks
such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy and many more.
autogluon
AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
Apache Ray Tune
H2O AutoML
Python: H2OAutoML(...)
Driverless AI
tpot looks like a good one
Platforms/Framework
Shared Resources of Models
DAGsHub DAGsHub is a web platform for data version control and collaboration for data scientists and machine learning engineers. It is like GitHub for data science and machine learning.
Kaggle
transformers
Experiment Tracking
wandb, fitlog, runx
Examples
https://github.com/h2oai/driverlessai-recipes
References
https://arxiv.org/pdf/1908.00709v1.pdf
https://towardsdatascience.com/an-example-of-hyperparameter-optimization-on-xgboost-lightgbm-and-catboost-using-hyperopt-12bc41a271e