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]
Conditional Variational Autoencoders
With code in Keras
The following slides are an overview of Variational Autoencoders. A notebook that modifies this to implement a Conditional Variational Autoencoder can be found below.
A Jupyter notebook with the implementation can be found here.