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Implementations for assignments of Stanford CS231n: Deep Learning for Computer Vision, Spring 2025

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# Introduction

This repository contains implementations for assignments of Stanford CS231n: Deep Learning for Computer Vision Spring 2025. Homepage of this course can be found [here](https://cs231n.stanford.edu/index.html). Course videos can be found [here](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv). (Though maybe a little outdated, but whatever, just for your reference :p)

\* News: Very happy that their team has uploaded the latest online course playlist on Sep 3 2025, now you should refer to [this](https://www.youtube.com/playlist?list=PLoROMvodv4rOmsNzYBMe0gJY2XS8AQg16)!

**DOWNLOADS**

- Assignment1: https://cs231n.github.io/assignments/2025/assignment1_colab.zip
- Assignment2: https://cs231n.github.io/assignments/2025/assignment2_colab.zip
- Assignment3: https://www.mediafire.com/file/az17sl7q7eroxi2/assignment3.zip/file

**OVERVIEW**

| Assignment  | Content                            | Tasks                                                                       | Dataset  |
| ----------- | ---------------------------------- | --------------------------------------------------------------------------- | -------- |
| Assignment1 | kNN                                | kNN, Cross Validation                                                       | CIFAR-10 |
|             | SoftMax Classifier                 | Softmax loss, SGD                                                           | CIFAR-10 |
|             | Two-Layer Neural Network           | Activation Functions, Forward/Backward Process                              | CIFAR-10 |
|             | Image Features                     | Training on Image Features                                                  | CIFAR-10 |
|             | Fully-Connected Neural Network     | Optimizing, Training Neural Networks                                        | CIFAR-10 |
| Assignment2 | Batch Normalization                | Batch Normalization, Layer Normalization                                    | CIFAR-10 |
|             | Dropout                            | Dropout, Regularization                                                     | CIFAR-10 |
|             | Convolutional Nets                 | CNN, Max-Pooling, Spatial Batch/Group Normalization                         | CIFAR-10 |
|             | PyTorch on CIFAR-10                | Training ConvNets with PyTorch, Module API, Sequential API                  | CIFAR-10 |
|             | Image Captioning with Vanilla RNNs | RNN Step Forward, Forward, Word Embedding                                   | COCO     |
| Assignment3 | Image Captioning with Transformers | Multi-Headed Attention, Position Encoding, Decoder Block, Sampling, ViTs    | COCO, CIFAR-10|
|             | Self-Supervised Learning           | Data Augmentation, SimCLR: Contrastive Loss, Finetuning                     | CIFAR-10 |
|             | DDPM                               | DDPM: Forward/Reverse Process, Unet, Classifier Free Guidance               | Emoji    |
|             | CLIP and DINO                      | CLIP: Similarity, Zero Shot Classifier, Image Retrieval, DINO: Segmentation | COCO, DAVIS|

**NOTES**

Feel free to view my blogs of implementation if you like!

- [CS231n Assignment1](https://zhuanlan.zhihu.com/p/1932835102196475401)
- [CS231n Assignment2](https://zhuanlan.zhihu.com/p/1935711302770205125)
- [CS231n Assignment3](https://zhuanlan.zhihu.com/p/1939293340622390307)

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