I’m not sure the title really nailed it well enough, but we are going to talk about solving VERY big problems as fast as we possibly can using highly sophisticated techniques. This blog article is really a high level overview of what you want to set up as opposed necessarily to the usual how to set it up. There are a ton of steps to the actual how to; I thought it best to just provide an overview in this article to what you want to do instead of how to do it.
If you are not familiar with Microsoft CoCos, you should be. Its a treasure trove of data for your learning pleasure! There just happens to be one pesky problem with it, and that is the fact that when attempting to find the files for training/testing; the Annotation file that ships with MS CoCo does not include the actual file name, but rather the image id. This sounds fine, except the data when you download it has a bunch of trailing stuff! In this article we will go through how to get it ready.
In this article I’m going to go through how to set up CNTK with Visual Studio Code and take advantage of those PASCAL GPUs I know everybody has these days. I will also do a breif overview of what CNTK and Visual Studio Code are and why they are so incredible for machine learning scientists.
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 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.