*Reproducible Deep Learning*

The first two exercises are out!

We start quick and easily, with some simple manipulation on Git branches, scripting, audio classification, and configuration with @Hydra_Framework.

Small
thread with all information 🙃 /n

Reproducibility is associated to production environments and MLOps, but it is a major concern today also in the research community.

My biased introduction to the issue is here: https://t.co/PqWH6uL5eT
The local setup is on the repository: https://t.co/9mhtZoJhE9

The use case for the course is a small audio classification model trained on event detection with the awesome @PyTorchLightnin library.

Feel free to check the notebook if you are unfamiliar with the task. /n
I spent some time understanding how to make the course as modular and "reproducible" as possible.

My solution is to split each exercise into a separate Git branch containing all the instructions, and a separate branch with the solution.

Two branches for now (Git and Hydra). /n
How well do you *really* know Git? The more I learn, the more I find it incredible.

I summarized most of the information on a separate set of slides: https://t.co/6dSmK3IfWB

Be sure to check them out before continuing! /n
Exercise 1 is a simple example of turning a notebook into a working script.

To make things more interesting, you have to complete the exercise while working on a separate Git branch!

https://t.co/35y9NQaVtz

Nothing incredible, but it is always good to start small /n
Once you have a working training script, it is time to add some "bell and whistles"!

My must-have is some external configuration w/ @Hydra_Framework. Exercise 2 guides you in all the required steps.

Plus side: colored logging!

https://t.co/WyAIMXASRl
That is all for the moment. The next exercises will explore having external data versioning with @DVCorg and complete isolation with @Docker.

You can follow by starring the official repository, or here on Twitter. 👀

https://t.co/9mhtZoJhE9

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"I really want to break into Product Management"

make products.

"If only someone would tell me how I can get a startup to notice me."

Make Products.

"I guess it's impossible and I'll never break into the industry."

MAKE PRODUCTS.

Courtesy of @edbrisson's wonderful thread on breaking into comics –
https://t.co/TgNblNSCBj – here is why the same applies to Product Management, too.


There is no better way of learning the craft of product, or proving your potential to employers, than just doing it.

You do not need anybody's permission. We don't have diplomas, nor doctorates. We can barely agree on a single standard of what a Product Manager is supposed to do.

But – there is at least one blindingly obvious industry consensus – a Product Manager makes Products.

And they don't need to be kept at the exact right temperature, given endless resource, or carefully protected in order to do this.

They find their own way.