Notes from DotNetFringe 2016

After quite some time on the road, I am finally back in San Francisco. My last stop on the way back was Portland, for the DotNetFringe conference. I’ll be totally honest: after 2 months away from home, I was a bit wiped out, and looking forward to some quiet time in my own place - not necessarily the best mindset heading to a conference. However, as it turns out, I ended up having a fantastic time there, and came back with a nice boost of energy.

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Try MBrace hassle-free with MBrace Minimal

I have been spending quite a bit of time lately with MBrace, a wonderful library that allows you to scale data processing or run heavy work-loads on a cloud cluster, using simple F# scripts. The library is very nicely documented, and comes with a Starter Kit project that contains all you need to provision a cluster, together with many scripts illustrating various use cases.

This is great, but… if you just want to play with the library and get a sense for what it does, it might be a bit initimidating. Furthermore, not everyone has an Azure subscription ready, which creates a bit of friction. So I figured, let’s try to create the smallest possible project that would allow someone to try out MBrace, without any Azure subscription needed.

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Kaggle Home Depot competition notes: model validation

In my last post, I discussed how the features of a machine learning model could be represented as simple functions, extracting a value from an observation. This allowed us to specify a model as a simple list of features/function, defining what information we want to extract from an observation. Today, I want to go back to where we left off, and talk about model validation. Now that we have a simple way to specify models, how can we go about deciding whether they are any good?

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Kaggle Home Depot competition notes: features

Against my better judgment, I ended up getting roped in entering the Kaggle Home Depot Search Relevance machine learning competition. As expected, this has been a huge time sink, and a lot of fun so far. One thing I found interesting is that this time I am working with a team. Having people to discuss ideas with is awesome; it is also an interesting opportunity to observe how others approach problems, and offers a chance to contrast methods and understand better what problem they are trying to address. In that frame, I thought I would try to put together some notes on recurring patterns I seem to repeat when setting myself up for this type of problem.

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Converting a DSL to Executable F# Code On-the-Fly, Part 2

In our previous post, we started attacking the following problem: we want our application to take in raw strings, representing code written in our own, custom domain-specific language, and convert them on the fly to F# functions, so that our use can change the behavior of the application at run time. In our particular example, to keep simple, we are simply trying to inject arbitrary functions of the form f(x) = (1 + 2 * x) * 3, that is, functions that take in a float as input, and return a float by combining addition and multiplication.

As a first step, we created an internal representation for our functions, using F# discriminated unions to model functions as nested expressions. This internal DSL gave us a type-safe, general representation for any function we might want to handle. However, we are still left with one problem: what we want now is to convert raw strings into that form. If we manage to do that, we are done: our user can, for instance, write functions in our own language in a text file, and have the application pick that file and convert it to F# code it can run.

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