|4 months ago||3 days ago|
|Jupyter Notebook||Jupyter Notebook|
|MIT License||Apache License 2.0|
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.
I created a way to learn machine learning through Jupyter
2 projects | reddit.com/r/learnmachinelearning | 30 Apr 2021
Looks cool. Also sounds like it would fit will with the 99 ML Projects repo. Maybe you could contribute here https://github.com/gimseng/99-ML-Learning-Projects
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
3 projects | reddit.com/r/selfhosted | 13 Jan 2022
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.
Help on creating a Federated Recommender System
1 project | reddit.com/r/MLQuestions | 17 Aug 2021
Or do I have to actually simulate the whole client server thing because thats how these frameworks do it - Flower and Pysyft .
Integration test: Complexity of privacy-preserving bird call bio-sensor for distributed ecological monitoring?
5 projects | reddit.com/r/SingularityNet | 23 Jun 2021
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.
Google Strikes Deal With Hospital Chain to Develop Healthcare Algorithms
1 project | reddit.com/r/medicine | 26 May 2021
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.
Is it even possible to have a service as "intelligent" as Google while still being privacy respecting?
4 projects | reddit.com/r/linuxquestions | 26 Apr 2021
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?
AIDungeon - Infinite adventures await!
fastai - The fastai deep learning library
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
QuantumKatas - Tutorials and programming exercises for learning Q# and quantum computing
river - 🌊 Online machine learning in Python
AugLy - A data augmentations library for audio, image, text, and video.
0xDeCA10B - Sharing Updatable Models (SUM) on Blockchain
frameworks - Sample code and build environments for MPC frameworks
common-voice - Common Voice is part of Mozilla's initiative to help teach machines how real people speak.
ETCI-2021-Competition-on-Flood-Detection - Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training