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<em>Among the blockchain and cryptocurrencies world, a lot of projects have seen the light. The most famous being Bitcoin and Ethereum, propelled by a wide media coverage. But one project has not made newspapers headlines yet is trying to achieve a tremendous work. As many blockchains are being built from scratch for lots of different needs all over the world, the Hyperledger Project launched by the Linux Foundation will try to standardize and democratize blockchain within the business world. Instead of letting companies solve their issues on their own, Hyperledger will combine a cross-industry knowledge to allow enterprises to build customized blockchains that would answer specific needs.</em>

WTF is Hyperledger ?

This article offers an approach to writing integration tests for Kafka-based applications that focuses on interaction specification, making tests more readable 

Kafka Message Testing: How to Write Integration Tests 

The article demonstrates the use of null-state analysis to remove compiler warnings of “check code for null safety”.

The 30-Day .NET Challenge, Day 9: Null Safety

Having been in software long enough to work across industries and with a variety of distributed systems, I know every system has its day. Software and systems engineers create performance requirements, latency constraints, and sweat the details on software quality. Their hard work ensures retail sites stay online through black Friday, ad campaigns survive the Superbowl, accounting systems make it through tax season, and media sites stay up to date during the election. And that’s not even counting safety critical systems we rely on each day, systems that keep our hospitals, our vehicles, and our industries humming.

Indebted to the Tech

<a href="https://mmhaskell.com/blog/2017/9/11/deep-learning-and-deep-types-tensor-flow-and-dependent-types" target="_blank">In the last</a> <a href="https://www.mmhaskell.com/blog/2017/9/18/checking-its-all-in-place-placeholders-and-dependent-types" target="_blank">couple weeks</a> we explored one of the most complex topics I’ve presented on this blog. We examined potential runtime failures that can occur when using Tensor Flow. These included mismatched dimensions and missing placeholders. In an ideal world, we would catch these issues at compile time instead. At its current stage, the Haskell Tensor Flow library doesn’t support that. But we demonstrated that it is possible to add a layer to do this by using dependent types.

Grenade! Dependently Typed Neural Networks

<a href="https://www.mmhaskell.com/blog/2017/8/21/digging-in-deep-solving-a-real-problem-with-haskell-tensor-flow">Last week</a> we built our first neural network and used it on a real machine learning problem. We took the Iris data set and built a classifier that took in various flower measurements. It determined, with decent accuracy, the type of flower the measurements referred&nbsp;to.

Putting the Flow in Tensor Flow!

Two weeks ago, we began our machine study in earnest by constructing a full neural network. But this network was still quite simple by deep learning standards. In this article, we’re going to tackle a much more difficult problem: image recognition. Of course, we’ll still be using a well known data set with well-known results, so this is only the tip of the iceberg. We’ll be using the <a href="http://yann.lecun.com/exdb/mnist/" target="_blank">MNIST data set</a>. This set classifies images of handwritten digits as the numbers 0–9. This problem is so well-known that the folks at Tensor Flow refer to it as the “Hello World” of machine learning.

Deeper Still: Convolutional Neural Networks

Last week we got acquainted with the core concepts of Tensor Flow. We learned about the differences between constants, placeholders, and variable tensors. Both the Haskell and Python bindings have functions to represent these. The Python version was a bit simpler though. Once we had our tensors, we wrote a program that “learned” a simple linear equation.

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