Let’s say you’re running an A/B test. Maybe you want to test how many conversions you will get if you change the design of the Signup page or the wording. You split users landing on your site into 2 groups – the control group and the experimental group. Those in the control group see the […]
Often with Natural Language Processing (NLP) applications a pipeline is useful to take the raw text and process it and extract relevant features before inputting it into a machine learning (ML) algorithm. Normalization From the standpoint of an ML algorithm, it may not make much sense to differentiate between different cases of a word – […]
This guide will be about setting up the fiddly bits when deploying a Jupyter Hub to an AWS instance. It won’t go into explicit detail about absolutely every step as the docs already do a great job of that. The purpose of this post is to discuss the things I found tricky after the install […]
This year has been like no other in living memory. Coronavirus has shaken our collective sense of what normality means and will undoubtedly continue to affect the world for years to come in one way or another. In this blog post I to try to gain some insights about the impact of Coronavirus around the […]
In a previous post, I walked through the maths of back-propagation (“backprop”). Here I will go through the implementation in Python (heavily based on Andrew Ng’s course). I’m going to use the alternative form equations (in the last blog post I denoted those with a tilde, but now I will drop that tilde). This means […]
Jupyter Notebooks are a great and widely used tool in data science. Quite often then run are run on localhosts or have to be accessed via SSH tunnelling. This is not very convenient when you want to share the results presented in the notebook to members of your team who are non-technical. In this post, […]
We can have a statistical model for single flip of the coin: $(\{0, 1\}, \{\sim\text{Ber}(p)\}_{p \in (0, 1)})$ (means the result is either 0 (tails) or 1 (heads) and the distribution is just a Bernoulli with prob $p$ of $1$ and $(1-p)$ of $0$) Then the null and alternative hypotheses are * $H_0$: coin is […]
I’m sure that every developer who has learned a little bit of ML dreams of applying this to the stock market and getting rich. At first glance RNNs and LSTMs seem perfect tools for the job. Something tells me it’s not going to be that easy, but it can be fun to try and this […]