The vision of this study group is to start with the top-level DataBlocks API and unravel each of the layers one by one until we hit the lowest layers.
So far we’ve looked at TfmLists, Datasets, Dataloaders, TfmdDl, Pipelines and also implemented a new CallBack. The videos are shared below.
Next upcoming zoom call: Saturday 7:30 AM IST Link-to be added.
This venture will serve three main purposes:
- We experiment with values other than defaults including different schedulers, optimisers, model architectures -and get an intuition about things that work and things that don’t work
- Get the capability to implement new research papers and ideas using the fastai library
- Learn some excellent software engineering tips and tricks from the fastai source code and take those ideas back to our workplaces/Kaggle etc
We will further be working for the community and releasing all videos as a group project such that those joining in the future and wanting to learn about code and underneath layers can do so by simply reading the blogs post/project summary/watching the videos.
Please join us in our journey into the fastai lower-level layers
You don’t need to be an expert in Python and Deep Learning, we definitely aren’t. But, wish to be.
Data.External : https://www.youtube.com/watch?v=FjfFx1wq3mQ (looks at
Datasets, Dataloaders and TfmdDL source code walkthru
TfmdLists complete walkthrough
Schedule for SG:
Week1: Study PETS tutorial and write a blog post about it with personal touches and twists. Also, participate in FastGarden using Fastai V2.
This is the central blog based of fastpages where you can contribute: Github Repo, Blog
Make sure to upload your post to your own blog first, and then to the above repo. You are the author and you deserve credit first!
Using separate Transforms for your training and validation sets in fastai2 : goes deep into
split_idxusage inside of fastai2’s low-level Datablock API