Uniform Manifold Approximation and Projection- UMAP
or how a Muggle can perform Math Magic....
I recently had to perform a large amount of dimensionality reduction - & as such needed to consider how to do it - in the end I went with UMAP. It is a relatively new technique so I figured that putting down some thoughts may be of interest.
Reading ML Papers as a newcomer - tips & tricks
Generative models can be described in ML terms as learning any kind of data distribution using unsupervised learning. Some examples that you might have seen include removing watermarks, transforming zebra into horses (and vice versa), and creating pictures of people who don't exist, among others. When I started diving in to this field, the range of methods, as well as what they could do, was confusing to me. After alot of research the simple taxonomy developed by Ian Goodfellow remains
Backwards & Backprop by choice
I am by no means an expert in ML. However, I am a former consultant and a newcomer to reading ML papers in a program that requires alot of reading them. So you could say that I am an expert in dealing with complicated content that I am not well versed in ;-) So, this week I thought I would put down the tips, tricks, hacks & approach that have helped me in tackling ML research papers.
OpenAI Scholars 2019 - The Syllabus
This week I really dug in to the background to DL - in particular loss equations, back prop & implementing them in vanilla python (look ma … no libraries!) It was about going backwards in more ways than one - and I am glad of it.
This week I started a 3 month program as a scholar at OpenAI - an amazing opportunity…..For reference, the syllabus I will be following is here: