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VectorInstitute / repository
This repository houses supplementary code and Jupyter notebooks that accompany the AI Pocket Reference project.
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This repository houses supplementary code and Jupyter notebooks that accompany the AI Pocket Reference project.
The repository is organized by main AI domains to align with the AI Pocket Reference project:
notebooks/
├── fundamentals/
├── nlp/
├── cv/
├── rl/
├── fl/
└── responsible_ai/
Each directory contains supplementary Jupyter notebooks for their corresponding Pocket References The notebooks follow the same naming convention as their pocket reference counterparts (without the .md extension). For example:
notebooks/nlp/lora.ipynb corresponds to the LoRA pocket referenceThis naming convention makes it easy to find the associated code examples and implementations for any given pocket reference.
You can run these notebooks in two ways:
Through Google Colab: Follow the Colab links provided directly in the pocket reference
Locally:
# Clone the repository
git clone https://github.com/VectorInstitute/ai-pocket-reference-code.git
cd ai-pocket-reference-code
# Set up your environment
pip install -r requirements.txt
# Launch Jupyter
jupyter lab
We welcome contributions of new notebooks and improvements to existing ones! Please see our CONTRIBUTING.md guide for details.
This project is licensed under the MIT License - see the LICENSE file for details.
Maintained by Vector AI Engineering
Selected from shared topics, language and repository description—not editorial ratings.
remotikal /
This repository houses a pre-trained neural network-based AI model created by Remotikal's Motion AI Neural Network. The AI model is built using popular deep learning libraries such as TensorFlow, Keras, PyTorch, and NumPy, and it leverages CUDA for accelerated computing.
40/100 health