Category: Machine Learning

Brendan Tierney continues a series on image classification:

In a previous post, I gave examples of how to label data using OCI Data Labeling. It was a simple approach to data labeling images for input to AI Vision. In that post, we just gave a label for the image to indicate if the image contained a Cat or a Dog. Yes, that’s a very simple approach, and we can build image classification models, and use the resulting model to predict a label for new images. These would be labeled as a Cat or a Dog with a degree of certainty. Although this simple approach can give OK-ish results, we typically want a more detailed model and predictions. For a more detailed approach, we can use Object Detection. For this, we need to prepare our data set in a slightly different way and Yes it does take a bit more time to prepare. Or perhaps it takes a lot more time to prepare the data. But this extra time in preparing the data should (in theory) give us a more accurate model.

This post will focus on creating a new labeled dataset using bounding boxes, and in a later post, we’ll examine the resulting model to see if it gives better or more accurate results.

Read on for the process.

Brendan Tierney separates the cats and the dogs:

In this post, I’ll build on the previous work on preparing data, to using this dataset as input to building a Custom AI Vision model. In the previous post, the dataset was labelled into images containing Cats and Dogs. The following steps takes you through creating the Customer AI Vision model and to test this model using some different images of Cats.

This post is part four of a series (first part, second part, third part) on custom image classification in Oracle.

Phil Booth takes a look at vector search systems:

Recently I built a system that uses vector search to logically truncate long documents and retain the most significant parts according to some search term. I’m a dummy, with no background in machine learning or mathematics, so there were new concepts for me to understand and implementation details to figure out. This post summarises what I learned.

Vector search and vector databases are becoming a fairly hot topic, so this at least grounds you on what they are.

Mala Mahadevan starts a newsletter:

I do my best to find trustworthy sources to learn from, but you know how it is – sometimes it’s tough to tell what’s legit. So, if you ever see me post something that seems a bit off, please cut me some slack. These aren’t necessarily my opinions, just things that caught my eye.

What I learn is just my take on what I heard or read. It might not always jive with what the original speaker or writer means, or understand. I don’t use any fancy AI bots like ChatGPT to help me out. I just quote stuff and break it down in my own words.

Mala focuses on a pair of videos. I snuck into the newsletter with a few bomb-throwing statements, particularly around anthropomorphism (the assignment of human or human-like qualities to non-humans). Anthropomorphism is extremely common in language. It’s all well and good as metaphor, but once you start to believe it for real, that’s when you end up in trouble.