I've been taking Stanford course CS231n: Convolutional Neural Networks for Visual Recognition while studying at GIKI. Here I have gathered my notes and solutions to assignments. The course lectures were recorded in Spring 2017, but the assignments are from Spring 2024.
I have organized the notes by assignment, including related helpers that were instrumental in solving the assignments. The notes also feature handwritten derivations and hand-drawn diagrams of backpropagation.
You can get starter code from here
- Q1: k-Nearest Neighbor classifier.
- Q2: Training a Support Vector Machine.
- Q3: Implement a Softmax classifier.
- Q4: Two-Layer Neural Network.
- Q5: Higher Level Representations: Image Features.
You can get starter code from here
- Q1: Multi-Layer Fully Connected Neural Networks.
- Q2: Batch Normalization.
- Q3: Dropout.
- Q4: Convolutional Neural Networks.
- Q5: PyTorch.
You can get starter code from here
- Q1: Image Captioning with Vanilla RNNs.
- Q2: Image Captioning with Transformers.
- Q3: Generative Adversarial Networks.
- Q4: Self-Supervised Learning for Image Classification.
- Q5: Network Visualization: Saliency Maps, Class Visualization, and Fooling Images.
- Q6: Image Captioning with LSTMs.
- Q7: Style Transfer
- Backpropagation for Batch Normalization
- Helped solidify my understanding of backpropagation
- StatQuest
- Assisted in understanding concepts that were not fully clear from the course lectures
- ResNet
- Helped solidfy my understanding of Residual Blocks and how they help in training deeper networks