Automatic Machine Learning

Automatic Machine Learning or "AutoML" is a field of Artificial Intelligence thats gaining a lot of interest lately. The idea is that doing any kind of task related to machine learning involves a whole lot of steps like cleaning a dataset, choosing a model, deciding what the right configurations of that model should be, deciding what the most relevant features are etc. The goal of AutoML is to automate all of that up to a point where all a data scientist would need to do is tell a machine to perform some task using a dataset and wait for it to learn how by itself. In this episode, i'm going to explain several popular AutoML techniques, then compare top AutoML frameworks like AutoKeras, Auto Sklearn, h20, Ludwig, etc. to help you decide which one will be the best for your needs.

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AutoML Framework #1 - MLBox
https://github.com/AxeldeRomblay/MLBox

AutoML Framework #2 - Auto Sklean
https://github.com/automl/auto-sklearn

AutoML Framework #3 - TPOT
https://github.com/EpistasisLab/tpot

AutoML Framework #4 - H20
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

AutoML Framework #5 - Autokeras
https://autokeras.com/

AutoML Framework #6 - Ludwig
https://uber.github.io/ludwig/

My video on Ludwig:
https://www.youtube.com/watch?v=w45t3itM5NM

The DARTS paper:
https://arxiv.org/abs/1806.09055

DARTS in PyTorch:
https://github.com/quark0/darts

Cool write-up on Simulated Annealing:
https://santhoshhari.github.io/simulated_annealing/

Cooler write-up on Bayesian Optimization:
http://krasserm.github.io/2018/03/21/bayesian-optimization/

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