Automatic Machine Learning or “AutoML” is a field of Artificial Intelligence – AI that’s gaining a lot of interest lately. The idea is that doing any kind of task related to Machine Learning – ML 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
AutoML Framework #2 – Auto Sklean
AutoML Framework #3 – TPOT
AutoML Framework #4 – H20
AutoML Framework #5 – Autokeras
AutoML Framework #6 – Ludwig
My video on Ludwig:
The DARTS paper:
DARTS in PyTorch:
Cool write-up on Simulated Annealing:
Cooler write-up on Bayesian Optimization:
Make Money with Tensorflow 2.0:
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Build a Healthcare Startup:
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