Tools to help deep learning researchers
12
Public repositories
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The GitHub presence of labmlai showcases a wide range of repositories dedicated to supporting deep learning researchers. With primary languages including Python, TypeScript, and Jupyter Notebook, notable projects like annotated_deep_learning_paper_implementations and labml facilitate both model training monitoring and educational resources for various deep learning topics.
Top languages
Public repositories
annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
labml
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
inspectus
LLM Analytics
python_autocomplete
Use Transformers and LSTMs to learn Python source code
app
Moved to https://github.com/labmlai/labml/tree/master/app
neox
Simple Annotated implementation of GPT-NeoX in PyTorch
chrome-extension
Source code of the papers.labml.ai Chrome extension
labml.ai
Our website
dashboard
Experiments dashboard for LabML
db
Minimalistic Object-Relational Mapper for JSON/YAML/Pickle file based db
research
No description provided for this repository.
app_cordova
Cordova wrapper for labml web app
Frequently asked questions
What does labmlai build on GitHub?
labmlai develops tools and resources aimed at deep learning researchers. Their repositories include implementations of deep learning papers, monitoring tools for model training, and educational projects utilizing Python, TypeScript, and Jupyter Notebook.
Which programming languages does labmlai use?
labmlai primarily uses Python and TypeScript across its repositories. Additionally, Jupyter Notebook is utilized for interactive educational content, while HTML and CSS support their web presence.
Are labmlai's repositories public?
Yes, all of labmlai's repositories are public on GitHub. This transparency allows researchers and developers to access, contribute to, and learn from their wide array of deep learning tools and implementations.
Is this exposure intended?
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