Deep Learning Match Making with Recurrent Networks

Hello World,

So I can’t disclose the problem statement, but what I can disclose is that the solution was a “Siamese LSTM”.  I was recommended to read Yan LeCun’s paper on “Siamese Networks” in which a shared weight convolutional neural network was used for facial recognition.  What I did was modify this concept for a forward reverse sweep recurrent (LSTM) network to match things together.

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Complex Neural Network Data Modelling with CNTK

dl_robo_arm

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This article is kinda exciting for me; because once you can internalize how this works, the world really becomes your oyster as far as what you can model with what kind of data.  In this example we are going to take some sample images and some random vector features and merge them together.  In a more realistic example you may take something like an image as well as some contextual tabular data and want to merge those two data sets together into a single prediction.

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Running Jupyter in Kubernetes with an SLA

jupyter_logo

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So Jupyter is a great tool for experimental science.  Running a jupyter notebook though can be tricky; especially if you want to maintain all of the data that is stored in it.  I have seen many strategies; but I have come up with one that I like best of all.  It is based on my “Micro Services for Data Science” strategy.  By using decoupled data and compute we can literally thrash our Jupyter notebook and all of our data and notebooks still live.  So why not put it in a self healing orchestrater and deploy via Kubernetes :D.

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Enterprise Scale Deep Learning on Azure with Tensor Flow

containership

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This article we will do a bit of a review of the technology stack required to enable this as well as the logistics behind setting it all up and operating against it.  The solution uses Azure Container Services, Docker, Kubernetes, Azure Storage, Jupyter, Tensor Flow and Tensorboard.  WOW!  That is a lot of technology; so we won’t do a deep dive how to but rather some pointers on how to get provisioned and then the high level process on how to use it.

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Optimizing ML Classification Algorithms

125k_coverage_bucket

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Today we are going to do a little exercise around optimizing an algorithm.  I was working with a customer who was using open data (and we know how that can be) to perform an initial set of predictions to show some value while adding in some collection capabilities so they can roll one with more reliable data later.

The data can be collected from here: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_pums_csv_2015&prodType=document

The sample notebook can be located here: https://github.com/drcrook1/MLPythonNotebooks

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Understanding Tensor Flow Input Pipelines Part 1

TFLogo

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Alright; so this whole input pipeline thing in pretty much every framework is the most undocumented thing in the universe.  So this article is about demystifying it.  We can break down the process into a few key steps:

  1. Acquire & Label Data
  2. Process Label Files for Record Conversions
  3. Process Label Files for Training a Specific Network Interface
  4. Train the Specific Network Interface

This is part 1.  We will focus on the 3rd item in this list; processing the files into TF Records.  Note you can find more associated code in the TensorFlow section of this git repository: https://github.com/drcrook1/CIFAR10

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Prepping Label Files for ML Training on Specific Machine

AzureDC

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So you likely will run into this at some point.  You are reading data from somewhere and it is relative path based; but that doesn’t necessarily always help load data in especially if you are storing data and your code in separate paths (which is common) or if you are sharing data with a team; or even if your data is just somewhere totally different.

Anyways; this article will help convert a .csv label file with actual named labels to a label file with full path with a numerical label that can be more easily one hot encoded during the reading process.  Note for deep learning often this is a two step process.  Step 1: Convert from relative pathing to specific pathing & numerical labels.  Step 2: Convert to framework specific storage format for input reading pipeline (which varies framework to framework).  Here we cover Step 1.  We will be using the CIFAR 10 data set which can be downloaded from here: https://www.kaggle.com/c/cifar-10/data

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Writing Files to Persisted Storage from PySpark

filesave

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So here is the big ticket item; How in the world do I write files to persisted storage from PySpark?  There are tons of docs on RDD.toTextFile() or things of that nature; but that only matters if you are dealing with RDD’s or .csv files.  What if you have a different set of needs.  In this case; I wanted to visualize a decision decision forest I had built; but there are no good bindings that I could find between PySpark’s MLLIB and Matplot lib (or similiar) to visualize the decision forest.

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High Performance, Big Data, Deep Learning at Scale

containership

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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.

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Dealing with Pesky Image Names in Cocos

CocoImage

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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.

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