Category: machine learning
-
PyTorch Lightning – Regression Example
I find there are a lot of tutorials and toy examples on convolutional neural networks – so many ways to skin an MNIST cat! – but not so many on other types of scenarios. So I’ve decided to put together a quick sample notebook on regression using the bike-share dataset. After learning the basics of neural…
-
KMeans clustering
Clustering is a type of unsupervised learning. Us humans would think of it as ‘categorization’ perhaps. For example, if I gave you a bag of red, blue and white balls and asked you to sort them (without telling you how) you would probably naturally gravitate towards sorting them by colour as this would be the…
-
How to – Principal Component Analysis
I always like to understand concepts well before I use them (which is good because it’s the right thing to do, but bad because it slows me down a lot!), so it was with great excitement that I came across Matt Brems’ article A One-Stop Shop for Principal Component Analysis recently. If you read this…
-
If you buy one book in 2020…
…make it Grokking Deep Learning by Andrew Trask! This gem of a book breaks deep learning down to its smallest component parts and then builds up your understanding from there. It’s the equivalent of stripping your car down to nuts and bolts and then re-building it: at the end, you will know to a certainty…
-
A goldmine of information…
I’ve just discovered the awesome Brandon Rohrer and his blog while trying to find an intelligible article on Bayesian inference. What a goldmine – this guy is a born educator! Thank you for sharing your knowledge – it is well-appreciated!
-
Getting results vs Understanding
Alexander Pope is famously quoted as saying: A little learning is a dangerous thing; drink deep, or taste not the Pierian spring: there shallow draughts intoxicate the brain, and drinking largely sobers us again. I’ve been thinking about these words the past few days as I worked on my latest challenge: a text classifier using…
-
Multivariate regression
So: with linear regression (aka simple linear regression) we have one feature which we are using to predict a dependent value (for example number of rooms as a predictor of house price). With multivariate regression (aka multiple linear regression) we simply have multiple features which could be used to predict that dependent value (for example…
-
Polynomial regression
Polynomial regression is a considered a special case of linear regression where higher order powers (x2, x3, etc.) of an independent variable are included. It’s appropriate where your data may best be fitted to some sort of curve rather than a simple straight line. The polynomial module of numpy is easily used to explore fitting the best…
-
Co-variance, Correlation & Linear Regression
Typically we have 2 sets of values and we want to find out if these 2 sets of values are related, and if so how, and by how much? Could height be indicative of weight? Could hours of practice be related to how many errors are made in a mathematical test paper? Co-variance is a…
-
Thoughts on strengthening machine learning in Africa
Yesterday’s Deep Learning Indaba X in Cape Town was so stimulating – there is such a diversity of activity going on in this field in South Africa – wow! The final debate on what we can do to strengthen machine learning in Africa really got me thinking about the role of EDUCATION and I have…
Sho't left to data science 