computer vision tutorials
A collection of Jupyter notebooks and tutorials for state-of-the-art computer vision models and techniques.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose it when you need focused few-shot image classification tutorials; it is narrower and covers that niche.
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
Choose it when you want time-tested classic CV recipes and code samples; it's older but covers established techniques.
Best Practices, code samples, and documentation for Computer Vision.
Choose it when you're fine-tuning models with Unsloth; it's a large set of notebooks but focused on that library and fine-tuning.
250+ Fine-tuning & RL Notebooks for text, vision, audio, embedding, TTS models.
These projects were analysed and named notebooks among their alternatives. The relationship is not symmetric — how notebooks rates them is a separate judgement, made when notebooks is analysed in its own right.
calls notebooks “Choose if you need computer-vision-specific examples for Roboflow workflows.”
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
calls notebooks “choose for practical computer vision model notebooks (YOLO/SAM) when your focus is CV, not general deep learning.”
Tutorials, assignments, and competitions for MIT Deep Learning related courses.