#Automl

The following slides are an overview of AutoML. This is an updated version of the slides presented at SuperComputing18. Additionally, this session covers an introduction to H2O for model selection and Comet.ml for hyperparameter optimization. Introduction to AutoML H2O H2O is a tool that allows you to perform Automated Machine Learning. A Jupyter notebook with an introduction to H2O can be found in the GitHub repository. The binder path to the repository is located here [Read More]

The slides below were used for presentations at the SuperComputing 2018 conference in Dallas.

Overview of PyTorch

The posts associated with these slides can be found here and here.

Quick introduction to AutoML

The post associated with these slides can be found here. Note that this is still work in progress and will be updated periodically.

Reader level: Intermediate Disclaimer: This post is work in progress and will be updated periodically. This is not meant to a comprehensive overview of the topic, but more of an introduction to AutoML, some tools and techniques. Overview Finding a model that works for a specific problem or a class of problems can be a time-consuming task. Usually, an engineer or a scientist determines what model class to use either based on his prior knowledge of the problem at hand or by evaluating several models and picking the best one. [Read More]