Tips from the other side: writing a thesis
After a relatively painless month or so of writing, I’ve finished my thesis. I’m yet to be examined, so whilst I can’t comment on how to write a good thesis, I’ve had a few thoughts on the process of getting from start to finish.
Three reasons you’re surprised that Trump won
Was Donald Trump’s election truly an upset? Some explanations from psychology for why we were more surprised than we ought to have been.
Deep Learning Practical 2: Decoding MNIST
MNIST (Mixed National Institute of Standards and Technology) is a database of handwritten digits. Compiled by Yann LeCun and colleagues, it’s a classic benchmark problem in machine learning. Pleasingly, you can write a model in Tensorflow that does a decent job of decoding the digits. Having got to grips with Python and some of the Tensorflow \[…\]
Deep Learning Book Chapter 3: Numerical Computation
This chapter was a bit of pot-pourri of things the authors wanted to tell us before we got on to machine learning proper. Much of it was straightforward, and then there were a few stingers, such as the bits about directional gradients. Typical problems we need to be aware of in using a digital computer to \[…\]
How others’ fortunes affect your happiness predicts generosity
This week we published a paper building on Robb Rutledge’s 2014 happiness model (my summary here), but asking whether we could also capture fluctuations in happiness that relate to what’s happening to other people around you.
Deep Learning Practical 1: Intro to Tensorflow
This week in the Deep Learning Reading Group we worked through some introductory exercises on Tensorflow, based upon the provided tutorials and edited by Zeb and I. It’s targeted at those with minimal Python experience, but who have programmed in Matlab. It’s largely self-explanatory; a collection of code examples with some discursive text, and a few snippets \[…\]