## Talks

Recent talks »

Recent talks »

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. »

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). »

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. »