Go

I’m rebooting this blog to bridge the gap between deep systems engineering and technical leadership

Machine Learning is often viewed as something akin to magic, only within the grasp of big companies like Facebook and Google who build machine learning models in Python using established frameworks like Scikit-learn or TensorFlow. So why on earth would you do this in Go? In this talk, James will try to dispel some of the myths and, with just a pinch of maths, build a basic ML model from scratch in Go. ...

In this blog post I will explore the steps to optimise a sparse vector dot product operation. We will start with a basic implementation in Go, convert it to assembler and then iteratively optimise it, measuring the effect of each change to check our progress. All the code from the post is available on Github and also forms part of the Golang Sparse matrix package. A vector dot product is a very common kernel, or basic building block, for many computations used within scientific computing and machine learning e.g. matrix multiplication, cosine similarity, etc. The dot product simply multiplies together the respective elements of two vectors and then adds all the results together to give a scalar output. ...

This is the third in a series of blog posts sharing my experiences working with algorithms and data structures for machine learning. These experiences were gained whilst building out the nlp project for LSA (Latent Semantic Analysis) of text documents. In Part 2 of this series, I explored sparse matrix formats as a set of data structures for more efficiently storing and manipulating sparsely populated matrices (matrices where most elements contain zero values). We tested the impact of using sparse formats, over the originally implemented dense matrix formats, using Go’s inbuilt benchmark functionality and found that our optimisations led to a reduction in memory consumption and processing time from 1 GB to 150 MB and 3.3 seconds to 1.2 seconds respectively. ...

This is the second in a series of blog posts sharing my experiences working with algorithms and data structures for machine learning. These experiences were gained whilst building out the nlp project for LSA (Latent Semantic Analysis) of text documents. In Part 1 of this series, I explored alternative approaches for representing and applying TF-IDF transforms for weighting term frequencies across document corpora. We tested the approaches using Go’s inbuilt benchmark functionality and found that our optimisations materially improved not just memory consumption but also performance (reducing memory consumption and processing time from 7 GB and 41 seconds to 250 KB and 0.8 seconds respectively). In this blog post I shall explore other areas for optimisation, seeking to further reduce memory consumption and processing time. ...

In my last blog post I walked through the use of machine learning algorithms in Golang to analyse the latent semantic meaning of documents. These algorithms, like many others in data science, rely on linear algebra and vector space analysis. By their nature, they often have to deal with large data sets, so any inefficiencies in the data structures used or algorithms themselves can result in a large impact on overall performance and/or memory usage. Inefficiencies that are negligable when working with small data sets can have a huge cost applied across extremely large datasets. As memory is a constrained resource, this could end up limiting the size of data sets that may be processed (certainly without having to resort to persistent storage and/or alternative algorithms) or the types of algorithms used. To this end, I decided to see if I could optimise the algorithms I used to consume less memory and improve processing performance without sacrificing too much functionality or accuracy. This is the first in a series of articles sharing my experiences benchmarking and optimising the algorithms and data structures used whilst building out the nlp project. ...

I spend a lot of time reading articles on the internet and started wondering whether I could develop software to automatically discover and recommend articles relevant to my interests. There are various aspects to this problem but I have decided to concentrate first on the core part of the problem: the analysis and classification of the articles. To illustrate the problem, lets consider the following string representing an article for the purpose of this example. ...

I, like many others, have been affected by the ongoing industrial dispute over Driver Only Operation (DOO) on Southern Railways. On some days this amounts to delayed or cancelled trains with extended journey times and the inconvenience of standing all the way into London and on others, like today, strikes leave no viable way of getting to work in London at all. There have been many attempts to measure and demonstrate the impact of the industrial action such as the use of the #todayimissed hashtag on Twitter/X (see below), a recent passenger survey conducted by The Association of British Commuters and even a tongue-in-cheek video game. Whilst certainly compelling, these have all largely been qualitative rather than quantitative. I have heard tales of people losing or missing out on jobs due to continued lateness or based on where they live and, more recently, quite a lot of people moving job or house so they avoid Southern Rail for their commute to/from work. This got me thinking and I started to wonder whether there was any correlation between the industrial action and property prices in the affected areas. ...

I am really enjoying programming in Google’s Go language (Golang for search engines) but very occasionally come across things that aren’t really possible, or considered idiomatic, in Go. Go is a very opinionated language which is a good thing as it keeps the language and tool chain very simple but also means if you need to do something unusual, it can sometimes feel like you are fighting the language. One example of this is developing plugins. ...

This is the start of my new blog. It is something I have been meaning to do for a long time but somehow never quite got around to. Part of the problem for me was choosing a blogging platform and tool chain - I was overwhelmed by the number of different options available. I know lots of people use platforms like Wordpress but I always liked the idea of static generators like Jekyll. ...