Showing posts from June, 2017

Automating the Machine Learning Workflow - AutoML

Motivation: Using Machine Learning will not require expert knowledge. All machine learning tasks follow the same basic flow. Difficult to find best fit hyper parameters. Hard to make hand made features. Fun. Automatic Machine Learning in progress: My motivation to write blog on this topic was Google's new project - AutoML . Google's AutoML project focuses on deep learning , a technique that involves passing data through layers of neural networks . Creating these layers is complicated, so Google’s idea was to create AI that could do it for them. There are many other open source projects, like AutoML and Auto-SKLEARN working towards a similar goal. Goal: The goal is to design the perfect machine learning “black box” capable of performing all model selection and hyper-parameter tuning without any human intervention.  AutoML draws on many disciplines of machine learning, prominently including Bayesian optimization - It is a sequential design strategy for gl