#Jupyter notebook

The MIT Introduction to Deep Learning offers a series of self-paced labs utilizing Google's Colaboratory for a cloud-based learning experience. Access essential resources such as Python notebooks and the 'mitdeeplearning' package, which are fundamental for completing lab exercises. Lecture slides and videos are openly available online, and the materials are distributed under the MIT License, promoting a thorough understanding of deep learning principles. Delve into these resources to advance skills in contemporary data science fields.

PyLabel is a Python package that assists in preparing image datasets for computer vision models like PyTorch and YOLOv5. It offers efficient conversion of annotation formats such as COCO to YOLO with minimal code. Users can analyze image datasets and strategically split them into training, test, and validation groups. Furthermore, PyLabel includes a Jupyter notebook-based tool supporting both manual and AI-assisted image labeling. It also allows easy visualization to verify annotations, developed as a project at UC Berkeley.