PySyft
AugLy
| PySyft | AugLy | |
|---|---|---|
| 7 | 14 | |
| 9,273 | 4,900 | |
| 0.8% | 0.2% | |
| 10.0 | 6.0 | |
| 3 days ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
AugLy
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Meta's A.I. exodus: Top talent quits as lab tries to keep pace with rivals
Their recent effort to generate training data for spotting stuff that includes unsanctioned narratives comes to mind. https://github.com/facebookresearch/AugLy
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Next steps for after classification
Data augmentation is usually helpful: https://github.com/facebookresearch/AugLy
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The hand-picked selection of the best Python libraries released in 2021
AugLy.
- Prefer volume or quality for BERT-based Text classification model
- Augly - An augmentation library for audio, image, video, and text from facebook
- [D] What's the best method to generate synthetic data for an image with text? Small dataset
- AugLy is opensourse now.
- Facebook is open-sourcing AugLy, a library that uses data augmentations to evaluate and improve ML models
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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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[N] Facebook AI Open Sources AugLy: A New Python Library For Data Augmentation To Develop Robust Machine Learning Models
Facebook Blog: https://ai.facebook.com/blog/augly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models/
What are some alternatives?
fastai - The fastai deep learning library
imgaug - Image augmentation for machine learning experiments.
openfl - The Open Flash Library for creative expression on the web, desktop, mobile and consoles.
speechbrain - A PyTorch-based Speech Toolkit
AIDungeon - Infinite adventures await!
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
openfl - An open framework for Federated Learning.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
Watermark-Removal-Pytorch - 🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.
river - 🌊 Online machine learning in Python