We will see how to model a deep learning network which can classify flowers into “roses” and “daisies”. This is a toy dataset and its purpose is to introduce you to the key concepts and methodologies.
- How to set up an end-to-end pipeline for training deep learning models
- Preprocessing techniques: Morphological transformations etc.
- Data augmentation using data generators
- Building a network: Ablation experiments, hyperparameter tuning, storing the best model in the disk etc.
This post explains the basic concepts used in the image classification model. If you can want to skip and jump directly to the code, here is the Github Repo.