My solutions and implementations for the Stanford CS231n course assignments. This repository contains implementations of various deep learning models, ranging from basic classifiers to state-of-the-art architectures like Transformers, Diffusion Models, and CLIP/DINO.

🔗 Course Website: https://cs231n.stanford.edu/
🔗 Assignments: https://cs231n.stanford.edu/assignments.html

The assignments are organized as follows, covering the fundamentals of neural networks to advanced generative and self-supervised models.

• k-Nearest Neighbor (kNN)
• Softmax Classifier
• Two-Layer Neural Network
• Image Features
• Fully-Connected Neural Network

• Batch Normalization
• Dropout
• Convolutional Neural Networks (CNN)
• PyTorch on CIFAR-10
• Image Captioning with Vanilla RNNs

• Image Captioning with Transformers
• Self-Supervised Learning
• DDPM
• CLIP and DINO