This repository is dedicated to various lab assignments for a deep learning course in 2023. Each lab focuses on different aspects and applications of deep learning.
- Handcraft implementation of a neural network, including feedforward and backpropagation.
- Emphasis on the complexity of implementing backpropagation manually.
- Implementing the TD (Temporal Difference) algorithm to play the Puzzle Game 2048.
- Utilization of n-tuple networks to reduce data volume.
- EEG signal prediction using a simple deep neural network created with PyTorch.
- Incorporation of EEGNet, which includes Depthwise Separable Convolution (deepwise conv + separable conv).
- Exploration of ResNet18, ResNet50, and other network architectures.
- Classification of images for Diabetic Retinopathy Detection into 5 classes.
- Using Conditional Variational Autoencoder (VAE) for predicting the future movement of a robotic arm.
- Application of the reparameterization trick to enhance model learning efficiency and performance.
- Handling of a significantly large dataset, making it the most challenging lab in the course.
- Implementation of Deep Q-learning (DQN) and Deep Deterministic Policy Gradient (DDPG) in a moon landing game environment to achieve secure spaceship landing.
- Application of DQN in the Atari breakout game.
- Utilization of the open-source Python library Gym.
- Addressing a traditional control system problem using Reinforcement Learning.
- Generating images under specific conditions using Conditional DDPM (Deep Deterministic Policy Model).
- Modification of the UNet architecture in DDPM.
- Working with the CLEVR dataset.