#Machine Learning

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]

Reader level: Introductory Introduction to Comet.ml Comet.ml is an API-driven framework for workflow management in Machine learning and Data Science experiments. Comet’s hyperparameter optimization is roughly based on the Advisor hyperparameter black box optimization tool. It allows you to add API calls from your code to perform optimization on a selected set of hyperparameters using Comet’s cloud service. This requires that you install the comet python package ‘comet_ml’. [Read More]

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.

Reader level: Introductory Table of Contents 1. Introduction 2. Overview of Neptune UI 3. How I used Neptune in my Keras ML project 4. What I have not covered Introduction Neptune.ml is a workflow management and collaboration tool for Data Science and Machine Learning (DS/ML). I have had the pleasure of testing this platform out for my own work and I must admit that I am convinced that every Data Science team needs something like this. [Read More]

This page contains slides to the ‘Introduction to Machine Learning’ NLI class series that I have taught at Virginia Tech.