in Spectroscopy on Quantum chemistry, Electronic structure, Rotational spectroscopy
Recently I’ve had to do a few calculations using Gaussian where I had to keep going back and waste time to find out how to do something again. I basically got fed up with this, and I’m organizing this post to more or less create a cheatsheet that tells you how, and which keywords to use for calculations using Gaussian ‘09/’16 to do with spectroscopy, and where to look for the outputs.
in Machine learning on Deep learning, Programming, Autoencoders
This is a curated list of what I would recommend as resources for learning about various aspects of deep learning, heavily inspired by this Github repository, although based on my own personal experience.
in Programming on Front-ends, Open-source, Dash, Web apps
Up until today, one of the primary methods of analyzing older data (actually stored on floppy disks!) in our lab was to use a specific Windows XP computer that runs a specific version of National Instruments LabView (7.0), which has a specific version of code that was written in the 2000’s specifically for this purpose.
in Machine learning on Deep learning, Autoencoders, Probabilistic models
Today, I thought I had a stroke of brilliance by starting to develop a Linear layer in PyTorch that would have its parameters drawn from a Gaussian. My idea was to implement a Bayesian neural network, where the parameters of the network are treated as probability distributions, rather than just simple point estimates. In terms of Bayes rule:
Here’s a quick post about a topic I find myself revisiting every few months: how to compress large batches of files efficiently. As I perform lots of calculations on a computing cluster, I like to routinely back things up at around publication time: that way I can have access to the data at the point where my paper was submitted, for example, and come back to it at a later date.