ML-YouTube-Courses VS Awesome-pytorch-list

Compare ML-YouTube-Courses vs Awesome-pytorch-list and see what are their differences.

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ML-YouTube-Courses Awesome-pytorch-list
309 2
14,354 14,985
0.9% -
7.2 0.0
4 months ago 3 months ago
Creative Commons Zero v1.0 Universal -
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.

ML-YouTube-Courses

Posts with mentions or reviews of ML-YouTube-Courses. We have used some of these posts to build our list of alternatives and similar projects.

Awesome-pytorch-list

Posts with mentions or reviews of Awesome-pytorch-list. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-19.

What are some alternatives?

When comparing ML-YouTube-Courses and Awesome-pytorch-list you can also consider the following projects:

squirrel-core - A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:

awesome-asyncio - A curated list of awesome Python asyncio frameworks, libraries, software and resources

Failed-ML - Compilation of high-profile real-world examples of failed machine learning projects

awesome-codex - A list dedicated to products, demos and articles related to 🤖 OpenAI's Codex.

Transformers-Recipe - 🧠 A study guide to learn about Transformers

500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code - 500 AI Machine learning Deep learning Computer vision NLP Projects with code

applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

awesome-document-understanding - A curated list of resources for Document Understanding (DU) topic

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python

3D-Machine-Learning - A resource repository for 3D machine learning

nlphoseGUI - This tool allows you to create Natural Language Processing pipelines for use with nlphose using a Blockly based GUI editor in any browser. As you create a pipeline it shows you the corresponding nlphose command which will execute the pipeline.

awesome-deep-learning - A curated list of awesome Deep Learning tutorials, projects and communities.