ETCI-2021-Competition-on-Flood-Detection
PySyft
| ETCI-2021-Competition-on-Flood-Detection | PySyft | |
|---|---|---|
| 1 | 7 | |
| 150 | 9,260 | |
| - | 0.7% | |
| 1.8 | 10.0 | |
| almost 2 years ago | 4 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Apache License 2.0 |
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ETCI-2021-Competition-on-Flood-Detection
PySyft
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A Better Mastodon Client
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.
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Just about conspirancy theories... Can you say this guy isn't rigth?
Something that maybe can help keeping sensor specs secret while still getting critical information out: https://github.com/OpenMined/PySyft
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I made a YT video showing how to host your own super accurate (microsecond) network time (NTP) server using the PPS output of a $12 GPS module
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.
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Help on creating a Federated Recommender System
Or do I have to actually simulate the whole client server thing because thats how these frameworks do it - Flower and Pysyft .
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Integration test: Complexity of privacy-preserving bird call bio-sensor for distributed ecological monitoring?
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.
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Google Strikes Deal With Hospital Chain to Develop Healthcare Algorithms
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.
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Is it even possible to have a service as "intelligent" as Google while still being privacy respecting?
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
What are some alternatives?
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
fastai - The fastai deep learning library
unet - unet for image segmentation
openfl - The Open Flash Library for creative expression on the web, desktop, mobile and consoles.
TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
AIDungeon - Infinite adventures await!
Pseudo-Labelling - Pseudo Labelling on MNIST dataset in Tensorflow 2.x
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
cellpose - a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
openfl - An open framework for Federated Learning.
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Watermark-Removal-Pytorch - 🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.