That website is generated automatically from the notebooks in this repo.
Take a look around -- all the things you'd hope to see in a high-quality project are there. It's all done for you by nbdev. For instance, see the nice README? Built from a notebook! github.com/fastai/nbdev/
nbdev v1 is already recommended by experts, and v2 is a big step up again.
"From my point of view it is close to a Pareto improvement over traditional Python library development." Thankyou @erikgaas 😀
Here's an example of the beautiful and useful docs that are auto-generated by nbdev+Quarto. nbdev.fast.ai/merge.html
Here's an example of an exported function in a notebook cell. This is automatically added to the python module, and the documentation you see on the right is auto-generated. nbdev.fast.ai/merge.html#nbd…
Every time we update a notebook to change the docs, library, or tests, everything is checked by @github Actions automatically
Here's the @pypi pip installer that's auto-generated. See the description? That's created for you from the notebook you use for your documentation homepage (just like the README, and the description for your conda package) pypi.org/project/nbdev/
I've barely scratched the surface in this brief tweet thread! For much more information, take a look at the blog post authored with @HamelHusain fast.ai/2022/07/28/nbd…
This launch wouldn't have been possible without some amazing people. I'd especially like to highlight Hamel & @wasimlorgat, who made nbdev2 a far better product than it would have been without them, JJ Allaire @fly_upside_down & the @quarto_pub team, & the @fastdotai community
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My keynote at JuliaCon, "Standing Out - What makes a programming language successful", is now available!
In it, I describe why I want Julia to succeed, and what is going to be needed to make this happen.
I coded in dozens of languages over the last few decades, and I'm starting to get a sense of what are the features that make some languages more successful than others.
I care about this for Julia because using a widely-used programming language has real benefits. More people can use your work more easily. And more of the stuff you need to do your work will have already been built for you!
I've just published over 20 hours of tutorials and live coding showing how to: install python the right way; set up a terminal; write shell scripts; use vim; use a remote Jupyter server; use git, github, tmux, and ssh; use the python debugger; and more! 🧵 youtube.com/playlist?list=…
Every session includes a forum discussion and also has youtube timestamps so you can see what's covered and jump to whatever interests you. Here's the forum links: forums.fast.ai/t/live-coding-…
The sessions also show how to succeed in a @kaggle competition, and how to become a power user of @HelloPaperspace to get GPU and @ProjectJupyter super-powers!
After 2 years, Practical Deep Learning for Coders v5 is finally ready! 🎊
This is a from-scratch rewrite of our most popular course. It has a focus on interactive explorations, & covers @PyTorch, @huggingface, DeBERTa, ConvNeXt, @Gradio & other goodies 🧵 course.fast.ai
For details on what's in this new course, check out the launch post: fast.ai/2022/07/21/dl-…
There are 9 lessons, and each lesson is around 90 minutes long. It's based on our 5⭐rated book, which is freely available online. Special hardware/software isn't needed—we show how to use free resources for everything. amazon.com/Deep-Learning-…
I led the team that studied mask efficacy in early 2020 and published our results in the Proceedings of the National Academy of Science.
I spent three months earlier this year revisiting this topic, and today I'm publishing my notes and links here: fast.ai/2022/07/04/upd…
An admission: these notes were meant to be the basis of another academic paper, and I gave up on it. In Jan 2022 when I finished this research, I looked around, and it seemed like no-one much cared about avoiding COVID any more.
So I figured it wasn't worth spending time on.
It seems like in the last couple of weeks there's signs that folks might be more open to protecting themselves and others by wearing a mask.
But the vast majority of public health advice I see on mask use is scientifically inaccurate. So I'm digging out this research for you.
Where things got slow is if you imported `fastcore.xtras`, which is a module that wraps a bunch of python stdlib functionality into some convenient interfaces. It's used by `fastcore.net`, `fastcore.parallel`, and `fastcore.utils`, so it comes up a lot.
But to do that, fastcore.xtras had to import a *lot* of stuff from python's stdlib!...
One of my fave chapters of "Practical Deep Learning for Coders", co-written with @GuggerSylvain, is chapter 8. I've just made the whole thing available as an executable notebook on Kaggle!
The chapter looks at the "matrix completion" problem at the heart of recommendation systems -- e.g what would you guess are the missing values in this matrix showing what rating users gave movies?
The key idea is to find the "latent factors" behind people's preferences