AlexNet Lab Assignment

This repository contains materials for a lab assignment designed for first-year students in computer science, artificial intelligence, or information science at the University of Amsterdam. The focus of the assignment is on understanding AlexNet, a pioneering deep learning model that revolutionized image classification.

Contents

• alexnet.ipynb: The primary Jupyter notebook for the lab assignment. This notebook guides students through the concepts behind AlexNet, including how it classifies images and how it learns features from data. The notebook is designed to be accessible to students with no prior programming experience, with explanations provided at an abstract level. Students will explore the inner workings of AlexNet, observe its behavior on a smaller dataset, and answer conceptual questions to reinforce their understanding.

• backend.py: A Python script containing helper functions used in the alexnet.ipynb notebook. These functions are abstracted away from the students to keep the focus on the conceptual learning rather than programming. This file ensures that the interactive parts of the notebook work seamlessly without requiring students to write or understand code.

How to Use

1. Opening the Notebook in Google Colab:

   • You can open the alexnet.ipynb notebook directly in Google Colab by following this link: Open in Colab.

2. Running the Notebook:

   • The notebook is designed to be run in sequence, with cells containing explanations, visualizations, and interactive components.
   • Students should read through each section carefully and answer the questions provided to deepen their understanding of how AlexNet functions.

License

This project is licensed under the MIT License - see the LICENSE file for details.