High Performance, Big Data, Deep Learning at Scale

containership

Hello World!

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.

Continue reading

Dealing with Pesky Image Names in Cocos

CocoImage

Hello World!

Coco_2

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.

Continue reading

Microsoft Cognitive Toolkit + VS Code = Awesome

CNTKIntellisense

Hello World!

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.

CNTKIntellisense

Continue reading

Operationalize Deep Learning with Azure ML

TeslaK80

Hello World!

So today we are going to do something really awesome.  Operationalize Keras with Azure Machine Learning.  Why in the world would we want to do this?  Well we can configure Deep Neural Nets and train them on GPU.  In fact, in this article, we will train a 2 depth neural network which outputs a linear prediction of energy efficiency of buildings and then operationalize that GPU trained network on Azure Machine Learning for production API usage.

Continue reading

Operationalizing SKLearn with Azure Machine Learning

web_servic-ified

Hello World!

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.

Continue reading

Arduino + Reinforcement Learning = Autonomous Robot

Herby_V1

Hello World,

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

Herby V1 simply learns to go forward as much as possible while avoiding obstacles.

Continue reading

The Cognitive Revolution

Hello World!

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.

Continue reading

Docker, Tensor Flow and Scientific Computing

Hello World,

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.

Continue reading

Standardize Continuous Data Shape for Neural Networks

Hello World,

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.

Continue reading