KalbeDigitalLab/Getting-Started-ML-CV
Practical guidlines with machine learning lifecycle especially in computer vision domains
This project offers a practical guide for machine learning engineers and data scientists to build, train, and deploy computer vision models. It walks you through the entire lifecycle, from analyzing and preparing image data to building a model, tracking experiments, and finally deploying it as a web application. You'll learn how to turn raw image datasets into functional, deployed AI solutions that can classify objects.
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Use this if you are a machine learning engineer or data scientist looking for a hands-on, step-by-step guide to developing and deploying computer vision models, particularly for image classification tasks.
Not ideal if you are not a developer and are looking for a no-code solution or a theoretical overview without implementation details.
Stars
16
Forks
7
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 16, 2023
Commits (30d)
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