machine learning

My perspective after a week of using ChatGPT: This is a step change in finding distilled information, and it’s only the beginning.

Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.

Lessons learned building a fish ID web app with fast.ai and Streamlit, in an attempt to reduce my fear of missing out on the latest deep learning developments.

Overview of a talk I gave at a deep learning course, focusing on AI ethics as the need for humans to think on the context and consequences of applying AI.

I got my first data science job in 2012, the year Harvard Business Review announced data scientist to be the sexiest job of the 21st century. Two years later, I published a post on my then-favourite definition of data science, as the intersection between software engineering and statistics. Unfortunately, that definition became somewhat irrelevant as more and more people jumped on the data science bandwagon – possibly to the point of making data scientist useless as a job title....

Everywhere you go these days, you hear about deep learning’s impressive advancements. New deep learning libraries, tools, and products get announced on a regular basis, making the average data scientist feel like they’re missing out if they don’t hop on the deep learning bandwagon. However, as Kamil Bartocha put it in his post The Inconvenient Truth About Data Science, 95% of tasks do not require deep learning. This is obviously a made up number, but it’s probably an accurate representation of the everyday reality of many data scientists....

I recently finished reading the book In Defense of Food: An Eater’s Manifesto by Michael Pollan. The book criticises nutritionism – the idea that one should eat according to the sum of measured nutrients while ignoring the food that contains these nutrients. The key argument of the book is that since the knowledge derived using food science is still very limited, completely relying on the partial findings and tools provided by this science is likely to lead to health issues....

I recently gave a talk about recommender systems at the Data Science Sydney meetup (the slides are available here). This post roughly follows the outline of the talk, expanding on some of the key points in non-slide form (i.e., complete sentences and paragraphs!). The first few sections give a broad overview of the field and the common recommendation paradigms, while the final part is dedicated to debunking five common myths about recommender systems....

In the past month, I’ve spent some time on my album cover classification project. The goal of this project is for me to learn about deep learning by working on an actual problem. This post covers my progress so far, highlighting lessons that would be useful to others who are getting started with deep learning. Initial steps summary The following points were discussed in detail in the previous post on this project....

I’ve been meaning to get into deep learning for the last few years. Now, the stars having finally aligned and I have the time and motivation to work on a small project that will hopefully improve my understanding of the field. This is the first in a series of posts that will document my progress on this project. As mentioned in a previous post on getting started as a data scientist, I believe that the best way of becoming proficient at solving data science problems is by getting your hands dirty....