99-ML-Learning-Projects VS PySyft

Compare 99-ML-Learning-Projects vs PySyft and see what are their differences.

99-ML-Learning-Projects

A list of 99 machine learning projects for anyone interested to learn from coding and building projects (by gimseng)
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99-ML-Learning-Projects PySyft
1 5
215 7,888
- 1.6%
0.0 10.0
4 months ago 3 days ago
Jupyter Notebook Jupyter Notebook
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

99-ML-Learning-Projects

Posts with mentions or reviews of 99-ML-Learning-Projects. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-30.

PySyft

Posts with mentions or reviews of PySyft. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-13.

What are some alternatives?

When comparing 99-ML-Learning-Projects and PySyft you can also consider the following projects:

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