Machine learning project with modular folders: 

This tutorial aims to define folder and Python files in a ML-based project. Each folder is designated for specific purposes, providing a clear separation of concerns and facilitating collaboration among team members.

Data Augmentation with TensorFlow and PyTORCH: 

This tutorial shows how to use the data augmentation technique in two frameworks, such as TensorFlow and PyTorch.

Transfer Learning with TensorFlow: 

This tutorial shows how to train and fine-tune a pre-trained model from the TensorFlow Repository and data augmentation techniques.