The post shares their journey of setting up a cost-effective CI/CD pipeline for AI applications using GitHub Actions and Docker. Emphasizing branching, versioning, and meticulous testing, the post highlights the importance of simplicity, efficiency, and automation in achieving a streamlined development workflow.
Category: machine learning
Cool Personal Datasets for Amateur Data Analysis
I’m an amateur data nerd. Problem is it’s hard to find good datasets that are personally relevant. However, it’s super easy to download some juicy data archives for data crunching at home. Here is a short guide to do this.
Analysing Your Tweets with ChatGPT
This post dives into applying ChatGPT's new Code Interpreter / Advanced Data Analysis to downloaded Twitter data. Keep reading for an insight into the cool things you can do.
Adventures in Voice Cloning
Smart speakers are everywhere. But could we design a smart speaker to talk to us with our own voice, or that of a person we know?
Getting Frames from YouTube
YouTube has a wealth of data but often you don't feel like downloading it all to disk. How can we stream video to python and then access the frame data?
Running Scripts at Startup
And other Linux delights. Often there is a need to configure processes to start on startup and to restart on failure. On Linux systems this process is controlled via the systemd init system and system manager. Now, as with most things Linux, there are a lot of sources of information out there. Several of them … Continue reading Running Scripts at Startup
Effortless NLP with spaCy
I've loved spaCy for a long time but I've only just got my head around how you can structure a text processing pipeline to take full advantage of its power.
The Neocortex & Power Iteration
A look at how the power iteration method for computing eigenvectors may allow brains to extract variance from the world.
Swift Taylor Approximations
This post continues my explorations of simple intelligence. In this post, we'll consider some elementary sensing cells. We'll then look at whether we can apply local function approximators. Sensors Consider a set of sensing cells, which we'll call "sensors". We have N sensors, where each sensor measures a value over time. This value could be … Continue reading Swift Taylor Approximations








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