Hi there 👋

In ML it’s common to measure generalization performance as the delta between the training, validation and test dataset. Training data is what your model actively optimizes against using some optimization procedure (e.g. SGD or conjugate gradient or quadratic programming). The validation data is used to get an unbiased estimate of the performance of the model on unseen data. Since the process of selection (hyper parameter selection, stopping criteria) invariably overfits the model to the validation data at the very end we get an estimate of model performance through the test dataset that should not be used for the process of training a model....

A neat use case for Raspberry Pi I was looking for a way to monitor my puppy’s dog crate and I figured a good approach would be to use my PS3 eye USB camera with my Raspberry Pi 4 to stream audio and video to my phone. Buying a dedicated monitoring handheld felt like a waste of money, since I already had the hardware to do it, in theory. Of course, in practice you run into issues and in this article I’ll describe the issues I ran into and how I solved them....

tl;dr: github.com/ricklamers/gpt-code-ui and to run it pip install gpt-code-ui && gptcode Alright, I’ll cut right to the chase. I’ve built GPT-Code UI because OpenAI couldn’t be bothered to give me access to their new fancy ChatGPT Code Interpreter. And most likely, you don’t have access either. What is ChatGPT Code Interpreter? Well, it’s basically a REPL (read–eval–print loop) that’s integrated with the coding abilities of ChatGPT. You can do really neat things with it like giving it a file (e....

CommonMark live testing tool https://spec.commonmark.org/dingus/ Hugo docs https://gohugo.io/documentation/ PaperMod docs https://github.com/adityatelange/hugo-PaperMod/wiki