PySyft VS fastai

PySyft: An open-source library developed by OpenMined that provides tools for building secure, privacy-preserving federated learning systems using PyTorch. Link

https://github.com/OpenMined/PySyft - Federated Learning data science

Incentives are much harder but smart contracts can handle the tech part.

Going this route eventually you quickly have "quantum AI app store" and your system of government is a 12GB download. Can't even say if it's a good idea compared to e.g. anarcho-primitivism.

Something that maybe can help keeping sensor specs secret while still getting critical information out: https://github.com/OpenMined/PySyft

Love this kind of project. To me this is just like https://github.com/open-quantum-safe/oqs-demos/ or https://github.com/OpenMined/PySyft or even k3s so often mentioned in this sub in the sense that I personally don't have a need for it. Yet I find it amazing that us, random curious geeks, have access to this kind of mind blowing technologies for basically free.

Or do I have to actually simulate the whole client server thing because thats how these frameworks do it - Flower and Pysyft .

Some of the technologies which could be integrated include differential privacy, distributed online machine learning, misinformation resilience and multi-party computation, all within the context of smart contracts and bioinformatics.

I think this is how it will be done. Look up PySift for how we can extract high-level insights from private datasets while preserving granular privacy.

What you are talking about is privacy-focused fed ML. Google FLOC is actually trying to achieve something similar. If you are interested in building something for yourself, check this out. https://github.com/OpenMined/PySyft

We are going to use fast.ai to create a simple classification which predicts whether a given image is a cat or not.

[(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/tduf4kyv9ezy5oz60pk1.png)](https://github.com/fastai/fastai)

I'd say the fastai library itself[0] is a pretty good example of how maintainable/scalable practices can come to life in notebook flows. There's something to be said IMO for an active project with 25.8k stars, 238 contributers, 2.7k commits, and 199 open vs 1.5k closed issues.

[0] https://github.com/fastai/fastai/

Jeremy Howard's YouTube Channel - Jeremy maintains the fastai library, which is an excellent package that will help anyone build complicated ML architectures in minimum time. His YouTube Channel has a number of free courses which do an amazing job of covering a variety of ML topics, and he also maintains a very active forum for people studying ML.

To create the AI we will use fastai. This is a python library, which is build on top of pytorch. No worries, you don't need to know how to code python. We will learn how this stuff works along the way :)

You may know fast.ai as a popular deep learning course. There is also a deep learning library with the same name (https://github.com/fastai/fastai) as well as software development tools like nbdev (https://nbdev.fast.ai/).

fast.ai has been offering education and tools for free for over 7 years, and has been approached by many companies asking for help. This program offers an avenue for business to get relevant professional services and support.

The "fastai" course is free, and does a really nice job walking you through building simple neural nets from the ground up:

https://github.com/fastai/fastai

What's going on here is the exact same thing, just much, much larger.

Development NB: https://github.com/fastai/fastai/blob/master/nbs/09_vision.augment.ipynb

There is already a ton of momentum around automating ML workflows. I would suggest you contribute to a preexisting project like, for instance, PyTorch Lightning or fast.ai.