It is explained in the fastai book in this chapter:
That chai feeling when you see Jeremy sharing new Kaggle notebooks.
That even more chai feeling when you realise he is 4 medals away from becoming a Notebooks grandmaster so we might get 4 more tutorials
Ignacio Oguiza (@oguiza here) is one of the primary references.
See the following refs:
Thanks for sharing
Hi @jkorte,
I’d say probably the easiest way to understand how this works is to take a look at this notebook which demonstrates how this technique can be used.
The timm package is truly great and also quite often updated with new architectures or adjustments to the existing ones.
make sure to install from master to make use of the cutting-edge SOTA models as soon as it’s added:
pip install git+https://github.com/rwightman/pytorch-image-models.git
for image segmentation, dose fastai library have a labeling tool for it?
How do I get the same presentation setup Jeremy is using in jupyter? I have not seen that functionality to switch between powerpoint and code
I had the same question, it’s pretty neat
No I haven’t seen a labeling tool for this. There are setups for image classification however
FastAI does not have a labelling tool, there a bunch of opensource ones available.
Why is a DataBlock
called a DataBlock
? I guess it’s a pretty hard thing to name, but I feel like a DataPreparation
would be a more intuitive name.
Can we get access to Jeremy’s slides before the course video is released? Or is the same content covered in the lesson videos/book/etc?
Notebooks are great:
Book written with notebooks - GitHub - fastai/fastbook: The fastai book, published as Jupyter Notebooks
Library written with notebooks - fastai/nbs at master · fastai/fastai · GitHub (using nbdev)
Blogs written with notebooks - https://fastpages.fast.ai
Presentations in notebooks- RISE — RISE 5.7.1
There are some variations in this iteration of the course versus the previous version. But the core concepts are the same.
I will ask Jeremy to post if he feels appropriate.