Occasionally I get asked questions in an interview or from students or some other place. When those questions come in written form, I like to reply to them on my blog for the rest of the world to be able to see. Today’s theme is about communication and communication technologies in a software engineering ecosystem. One item of interest is that our teams are constantly becoming multi-cultural, multi-timezone, more distributed and more diverse. This has its benefits, but from a communications perspective, it does have its challenges as well.
So today I have decided to actually begin a write up on where I’m headed. Its taken a long while to come up with what my next adventure after food trucks was going to be, especially with all of the things you can do with embedded technologies and machine learning. So here it is, a self driving race car.
In this article I aim to lay out the high level plan of attack for what my team and I are building and some of the direction on where we are headed.
So I just completed an incredible project with Brain Thermal Tunnel Genix, where I learned so much about pattern recognition, machine learning and taking research and algorithms and pushing those into a production environment where it can be integrated into a real product. Today’s article takes those lessons and provides a sample on how to perform complex modelling and operationalize it in the cloud. The accompanying Gallery Example can be found here.
So there are a ton of articles out there on the theory of Reinforcement Learning, but very few with an actual application. I watched a few lectures from Berkley, and read a few articles by NVidia and thought, “Well, lets just give this a shot”. 8 hours later, this is what I had.
Herby V1 simply learns to go forward as much as possible while avoiding obstacles.
So I’ve been on a bender with this Python thing not having proper functional piping. I just can’t beleive it was not built in. Every language should have this. It cleans up your code so much its unbeleive-able. Fear not however, I have built one for Python :D. Now I just need to figure out how to create pip packages. Anyways, lets just do a quick walk through on what it is, how it works etc.
So here is a pretty raw blog article; not unlike most of my articles. The cognitive revolution. I’m going to coin this term today. What the heck is this thing? What does it mean for you? What does it mean for me? Where did it come from? What is it? These are questions I aim to answer in this blog article.
So this blog post is to get you operational with Docker, image and volume management with a pivot towards scientific computing and tensor flow. So I am working on building a Jupyter Notebook for the local mahcine learning meetup to learn the ins and outs of Tensor Flow and deploy this thing up to Azure. Part of getting this to work is not only managing the Docker Containers, but also the data on the volumes so when we deploy up to Azure and somebody opens up the notebook it comes pre-loaded with all the necessary tutorial data.
This is a short post. Basically I had a data set come in, where there were some funky characters involved. I was getting “Can’t read this; doesn’t appear to be UTF-8”. Looked around on stackoverflow for a while to little avail. I came up with this, which works.
So this is an interesting problem. You are collecting data from somewhere and you want to feed it into a neural network for classification. There is one main problem with this. The shape of the data! Neural networks and really just anything require specifically shaped data, you can’t just like give it something of ambiguous size. There are tons of papers out there on dimensionality reduction, but nothing on dimensionality reduction to a specified size. This article explains my approach.