I just put together a little tutorial about PyTorch DataLoaders and collate_fn that I wanted to share with you.
I think It will help everyone trying to understand a bit better the inner workings of PyTorch dataloaders in general and the collate_fn in particular.
I present a very concrete example with toy data to explain what “collate” actually means and also show how to implement a custom collate function to make dataloading work with sequences of different length in an efficient way.
Take a look at it and of course any feedback, comments, ideas are more than welcome
Sharing another blog post made. Created a spam detection model using tabular data with different types of models. Take a look if you’re interested. Roughly coincides with Lesson 6:
Want to share a LLM application that aims to help the workflow of whoever needs to write something easier.
Motivation of building:
We took @jeremy 's advice, and want to share and help whoever may find themselves in a similar shoes a few months ago.
How do we do that? Through learning by doing, learning by writing, and learning by sharing!
Hello All,
Based on the Lesson 1 i made a Chipa or Donut classifier. “Chipa” is a type of small bread-like baked good that’s popular in Paraguay and other parts of South America.
After a long break from ML I’m refreshing my skills with fast.ai again. here is a simple car tyre classifier to identify illegal and legal tyres. The predictions works quite well considering the small data sample. Lots to do to improve this.
Hi all! I’m Tony. I’m a robotics engineer with a background in Electrical Engineering, who has found himself doing more and more software as time goes on.
After a long time of wanting to learn more about ML and Deep Learning, I finally started actually going through the course today. For a first project, I decided to build off the “Bird or Not” example. Since my kids are super into card games, I trained a classifier to predict if a given image is showing a card from Magic: Tha Gathering, Pokemon, or One Piece
I was a little shocked to find that by fine-tuning a pre-trained resnet model, even with pretty cruddy data that was scraped from the internet, and doing no QA on my side, the classifier can do a pretty decent job with just a few rounds of fine tuning! I was also shocked to see that fine tuning is pretty quick, even using only a CPU with no acceleration! (At least for this model)
While it isn’t a lot to look at, and nothing new to folks who have done the first week of the course, I’m using this as an opportunity to share and introduce myself to the folks on the forum!
