For my Deep Learning assignment, I worked on a computer vision problem that gave me practical experience with image data and neural networks.

The project took me through data preparation, model design, training, validation and performance evaluation. Working through these stages helped me understand how decisions made during development affect the model’s results.

A key part was exploring how Convolutional Neural Networks (CNNs) learn visual features from images. Applying these concepts made the architecture easier to understand and gave me a clearer picture of how a network turns image inputs into predictions.

Evaluating the model was just as useful as training it. I examined its performance, considered the risk of overfitting, and explored how training choices influenced its ability to handle unseen images.

The assignment helped me connect the theory covered in the module with the practical work of building a computer vision model. It also reinforced the importance of checking results carefully before drawing conclusions about how well a model performs.