top-10-automated-machine-learningauto-ml-tools
machine-learning-models-with-just-a-few-lines
in particular
- automate-the-exploratory-data-analysis
- https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.background_gradient.html
- AI code assistant for pandas https://github.com/approximatelabs/sketch
nice overview of basic concepts in ML & DL https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-deep-learning
nice review of ANNs types https://www.asimovinstitute.org/author/fjodorvanveen/
free online book: http://d2l.ai/
boook: https://christophm.github.io/interpretable-ml-book/
table of 4 main techniques, in particular:
- https://neptune.ai/blog/shap-values
- https://www.oreilly.com/content/introduction-to-local-interpretable-model-agnostic-explanations-lime/
- libraries with -- many techniques already implemented:
https://poloclub.github.io/ganlab/
http://experiments.mostafa.io/public/ffbpann/
ANN Overfitting-Regularization
8-types-of-time-series-classification-methods
Kolmogorov Smirnov for TS clustering
https://www.analyticsinsight.net/top-10-python-libraries-for-time-series-analysis-in-2022/, in particular
- https://www.sktime.net/en/stable/
- https://tsfresh.readthedocs.io/en/latest/
- https://unit8co.github.io/darts/
- libraries
https://link.springer.com/article/10.1007/s00521-023-08459-3#Sec13
https://github.com/jainyk/package-outlier ( https://towardsdatascience.com/5-outlier-detection-methods-that-every-data-enthusiast-must-know-f917bf439210 )
https://neptune.ai/blog/hyperparameter-tuning-in-python-complete-guide , in particular:
- https://docs.ray.io/en/latest/tune/index.html
- https://hyperopt.github.io/hyperopt/
- https://optuna.org/
https://www.datasciencelearner.com/top-5-python-library-for-operational-research/