8 entries
In this notebook, we'll use the MovieLens 10M dataset and collaborative filtering to create a movie recommendation model. We'll use the data from movies.dat and ratings.dat to create embeddings that will help us predict ratings for movies I haven't watched yet.
I don't know anything about rice disease but apparently these are various rice diseases and this is what they look like. Jeremy Howard, Fast.ai Course Lesson 6 I have no idea if Jeremy had this in mind when he said this (alluding to the fact he doesn't know about the subject area, but when...
Having completed lesson 5 of the FastAI course, I prompted Claude to give me some good datasets upon which to train a random forest model. This housing dataset from Kaggle seemed like a nice option, so I decided to give it a try. I am also going to try something that Jeremy Howard recommended for...
In this notebook/post, we're going to be using the markdown content from my blog to try a language model. From this, we'll attempt to prompt the model to generate a post for a topic I might write about.
In this notebook, we train two similar neural nets on the classic Titanic dataset using techniques from fastbook chapter 1 and chapter 4.
Some quotes from Lesson 3 of course.fast.ai by Jeremy Howard. I remember a few years ago when I said something like this in a class somebody on the forum was like "this reminds me of that thing about how to draw an owl". Jeremy's basically saying okay step one draw two circles, step two draw the...
The following is the notebook I used to experiment training an image model to classify types of rowing shells (with people rowing them) and the same dataset by rowing technique (sweep vs. scull). There are a few cells that output a batch of the data. I decided not to include these because the...
I set out to do a project using my learnings from the first chapter of the fast.ai course. My first idea was to try and train a Ruby/Python classifier. ResNets are not designed to do this, but I was curious how well it would perform.