AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Google Brain researchers published this amazing paper, with accompanying GIF where they show the true power of AutoML.

AutoML stands for automated machine learning, and basically refers to an algorithm autonomously building the best machine learning model for a given problem.

This task of selecting the best ML model is difficult as it is. There are many different ML algorithms to choose from, and each of these has many different settings ([hyper]parameters) you can change to optimalize the model’s predictions.

For instance, let’s look at one specific ML algorithm: the neural network. Not only can we try out millions of different neural network architectures (ways in which the nodes and lyers of a network are connected), but each of these we can test with different loss functions, learning rates, dropout rates, et cetera. And this is only one algorithm!

In their new paper, the Google Brain scholars display how they managed to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. Using evolutionary principles, they have developed an AutoML framework that tailors its own algorithms and architectures to best fit the data and problem at hand.

This is AI research at its finest, and the results are truly remarkable!

GIF for the interpretation of the best evolved algorithm

You can read the full paper open access here: (quick download link)

The original code is posted here on github:

GIF for the experiment progress

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