New video series out this week (and into next!) on the @weights_biases YouTube channel.

They're Socratic livecoding sessions where @_ScottCondron and I work through the exercise notebooks for the Math4ML class.

Details in 🧵⤵️
Socratic: following an ancient academic tradition, I try to trick @_ScottCondron into being wrong, so that students can learn from mistakes and see their learning process reflected in the content.
(i was inspired to try this style out by the @PyTorchLightnin Master Class series, in which @_willfalcon and @alfcnz talk nitty-gritty of DL with PyTorch+Lightning while writing code. strong recommend!)

Math4ML: in the class, we cover core ideas from linear algebra, calculus, and probability that are useful in ML.

I try to emphasize the key intuitions and connect them to programming ideas: shapes are like types, big O-notation and limits, etc.

wandb.me/m4ml-videos
Exercise notebooks: the M4ML class has always included GitHub-backed Colab/binder notebooks with text and exercises that firmed up the ideas in the lectures, but there wasn't any public video content explaining how to use them. Until now!

github.com/wandb/edu/tree…
Livecoding: the exercises are code, and we write the solutions together live (with light editing to remove typos etc)

Because the exercises are code, they can be graded programmatically.

Essentially, each comes with unit tests that you have to pass. Failures generate hints!
This course material is designed for remote, asynchronous online education.

The combination of video lectures, recorded homework sessions, and self-grading exercises is meant to make it possible to get the full benefit of the course asynchronously via the internet.
And if you have questions that the videos and autograder can't answer, you can post about them on the YouTube channel or in the W&B forum: wandb.me/and-you

The first video will be out tomorrow! I hope to see you there.

wandb.me/m4ml-exercises…

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More from @charles_irl

24 Aug
If you're like me, you've written a lot of PyTorch code without ever being entirely sure what's _really_ happening under the hood.

Over the last few weeks, I've been dissecting some training runs using @PyTorch's trace viewer in @weights_biases.

Read on to learn what I learned!
I really like the "dissection" metaphor

a trace viewer is like a microscope, but for looking at executed code instead of living cells

its powerful lens allows you to see the intricate details of what elsewise appears a formless unity

kinda like this, but with GPU kernels:
number one take-away: at a high level, there's two executions of the graph happening.

one, with virtual tensors, happens on the CPU.

it keeps track of metadata like shapes so that it can "drive" the second one, with the real tensor data, that happens on the GPU.
Read 9 tweets
31 Jul 20
another great regular online talk series! they're talking about GPT-3 now
@realSharonZhou: sees opportunities in medicine for with "democratization" of design of e.g. web interfaces.

this could be key for healthcare providers who have clinical expertise and know what patients need but don't have web design skills.
@DrHughHarvey sees this as a step towards the holy grail of ML in radiology: a model that takes in an image and returns a full radiology report.

jump from GPT2 to GPT3 was just size. what might trillion-parameter models bring in other domains?
Read 8 tweets
24 Jul 20
1/hella

this 🧵 by @daniela_witten is a masterclass in both the #SVD and in technical communication on Twitter.

i want to hop on this to expand on the "magic" of this decomposition and show folks where the rabbit goes, because i just gave a talk on it this week!

🧙‍♂️🐇💨😱
tl;dr: the basic idea of the SVD works for _any_ function.

it's a three step decomposition:

- throw away the useless bits ⤵
- rename what remains 🔀
- insert yourself into the right context ⤴
also, if you're more of a "YouTube talk" than a "tweet wall" kinda person, check out the video version, given as part of the @weights_biases Deep Learning Salon webinar series

Read 19 tweets

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