I had an email from a UK high school student, Molly Patterson, who was researching “Are Science and Technology Going too Far?” for a project. She asked some questions, which I thought were interesting. Molly agreed that I could share her questions and my answers on the blog. Here they are.

I was selected as recipient of the 2016 Junior Scientific Award by the Complex System Society!

EDIT: A complete revamp of PyTorch was released today (Jan 18, 2017), making this blogpost a bit obselete. I will update this post with a new Quickstart Guide soon, but for now you should check out their documentation.</a>

We have many different ways of delivering the Lateral API to clients who would like to install it in their own environment. One of those is as an Azure VHD for deployment to Azure VMs. In this post I will cover how to create a VHD that is fully compatible with Azure from an Ubuntu Cloud Image base.

Application of models to data is fraught. You are faced with collaborators who sometimes have a very basic understanding of the complications of collating, processing and curating data. Challenges include: poor data collection practices, missing values, inconvenient storage mechanisms, intellectual property, security and privacy. All these aspects obstruct the sharing and interconnection of data.

These days, you’d have to be mentally unstable to attempt to run across a freeway. If you attempted it today, even if you are an expert Frogger player, your life expectancy would be measured in seconds. But how does this change in an autopilot rich future? If you’re an asshat, and you wanted to, you could just run across the road, safe in the knowledge that the cars would stop (or swerve) to avoid hitting you.

Most proofs in optimization consist in using inequalities for a particular function class in some creative way. This is a cheatsheet with inequalities that I use most often. It considers class of functions that are convex, strongly convex and $L$-smooth.