You might notice that the in-built Python hash function does not work with dictionaries. That’s for good reason because it can be inconsistent across platforms. In this post, I talk about a simple method using standard libraries to hash a Python dictionary in a more stable manner.

There many methods for handling configuration files within a project and it can be difficult to find a solution that works well. In this post, I talk about a small library that allows argparse to work well across multiple files while still providing the expected argparse features such a help pages.

Sometimes you might want to run unittests from another Python script and gather its results instead of running python3 -m unittest manually. Running test cases programmatically is quite easy and gives you a lot of control on what happens during or after running the test cases.

Spiking neural networks work by simulating the membrane potential of biological neurons. Unlike artificial neural networks spiking ones attempt to model the biological neurons that build up our brains. So while ANNs are inspired by biological brains, SNNs try to create them.

MapReduce requires the data chunks to be processed independently. This processing model is unsuitable for many graph models in which a calculation often requires knowledge about calculations done for other nodes. This issue is exactly what Pregel tackles with message passing.

Configuration files can get messy when dealing with a large number of external parameters. The built-in solutions like argparse might not be scalable or clean enough to manage external parameters. In this post I look at an alternative YAML based solution that implicitly passes configuration to functions.

A very quick introduction to Keras and common architecture types with short examples. The slides include code snippets and tips on training neural networks.

I cover useful information from running batch jobs to introduction for TensorFlow and Keras. These are aimed at students who have machine learning based projects.

Virtualenv provides a very good way to isolate packages. But sometimes it is better to install packages shared across projects in a custom location.