Awesome-VAEs
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models. (by matthewvowels1)
stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning (by afshinea)
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| Awesome-VAEs | stanford-cs-229-machine-learning | |
|---|---|---|
| 1 | 1 | |
| 755 | 16,526 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 3 years ago | almost 4 years ago | |
| - | MIT License |
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.
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.
Awesome-VAEs
Posts with mentions or reviews of Awesome-VAEs.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-29.
-
VAEs
List of VAE projects/works: https://github.com/matthewvowels1/Awesome-VAEs
stanford-cs-229-machine-learning
Posts with mentions or reviews of stanford-cs-229-machine-learning.
We have used some of these posts to build our list of alternatives
and similar projects.
What are some alternatives?
When comparing Awesome-VAEs and stanford-cs-229-machine-learning you can also consider the following projects:
PyTorch-VAE - A Collection of Variational Autoencoders (VAE) in PyTorch.
machine-learning-roadmap - A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
awesome-datascience - :memo: An awesome Data Science repository to learn and apply for real world problems.
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
benchmark_VAE - Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
modern-php-cheatsheet - Cheatsheet for some PHP knowledge you will frequently encounter in modern projects.