Deep-learning

Deep Learning Project A1

Overview

This repository contains our experiments in deep learning, including classification, regression, and generative modeling tasks using Keras and TensorFlow. The work was carried out on datasets like Fashion MNIST, CIFAR-10, and custom image datasets.

Objectives

• Experiment with various network architectures (MLP, CNN) on image datasets.
• Perform hyperparameter tuning for model optimization.
• Develop generative models including CAE, VAE, and GAN.
• Explore the application of deep learning techniques in real-world contexts.

Team

• Chloé Tap
• Evan Meltz (me)
• Giulia Rivetti

Repository Structure

• Part1_Fashion: Contains the Fashion MNIST deep learning code.
• Part1_CIFAR: Contains the CIFAR-10 experiments.
• Part2: Contains code for additional tasks.
• Part3: Contains code for generative model experiments.
• Assignment_Documentation: Contains the assignment instructions and report.