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Shortest solutions for CS231n 2021-2026
| Date | Stars |
|---|---|
| 2026-07-24 | 486 |
| 2026-07-25 | 486 |
| 2026-07-28 | 486 |
| 2026-07-30 | 486 |
| 2026-08-06 | 486 |
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<h1 align="center">CS231n: Assignment Solutions</h1> <p align="center"><b>Convolutional Neural Networks for Visual Recognition</b></p> <p align="center"><i>Stanford - Spring 2021-2025</i></p> ## About ### Overview Solutions for **CS231n** course assignments offered by Stanford University (Spring 2021-2025). Inline questions are explained in detail, the code is brief and commented (see examples below). From what I've checked, these are the shortest code solutions. > Check out the solutions for **[CS224n](https://github.com/mantasu/cs224n)**. They contain more comprehensive explanations than others. ### Main sources (official) * [**Course page**](http://cs231n.stanford.edu/index.html) * [**Assignments**](http://cs231n.stanford.edu/assignments.html) * [**Lecture notes**](https://cs231n.github.io/) * [**Lecture videos** (2025)](https://www.youtube.com/playlist?list=PLoROMvodv4rOmsNzYBMe0gJY2XS8AQg16) <br> ## Solutions ### Assignment 1 * [Q1](assignment1/knn.ipynb): k-Nearest Neighbor classifier * [Q2](assignment1/softmax.ipynb): Implement a Softmax classifier * [Q3](assignment1/two_layer_net.ipynb): Two-Layer Neural Network * [Q4](assignment1/features.ipynb): Higher Level Representations: Image Features * [Q5](assignment2/FullyConnectedNets.ipynb): Fully-connected Neural Network ### Assignment 2 * [Q1](assignment2/BatchNormalization.ipynb): Batch Normalization * [Q2](assignment2/Dropout.ipynb): Dropout * [Q3](assignment2/ConvolutionalNetworks.ipynb): Convolutional Networks * [Q4](assignment2/PyTorch.ipynb): PyTorch on CIFAR-10 * [Q5](assignment3/RNN_Captioning.ipynb): Image Captioning with Vanilla RNNs ### Assignment 3 > [Download Original](https://github.com/mantasu/cs231n/issues/16#issuecomment-3046063900) * [Q1](assignment3/Transformer_Captioning.ipynb): Image Captioning with Transformers * [Q2](assignment3/Self_Supervised_Learning.ipynb): Self-Supervised Learning for Image Classification * [Q4](assignment3/DDPM.ipynb): Denoising Diffusion Probabilistic Models * [Q5](assignment3/CLIP_DINO.ipynb): CLIP and Dino ### Previous Years * [A1](assignment1/svm.ipynb): Training a Support Vector Machine * [A2](assignment2/TensorFlow.ipynb): TensorFlow on CIFAR-10 * [A3](assignment3/Network_Visualization.ipynb): Network Visualization: Saliency Maps, Class Visualization, and Fooling Images * [A3](assignment3/Generative_Adversarial_Networks.ipynb): Generative Adversarial Networks * [A3](assignment3/LSTM_Captioning.ipynb): Image Captioning with LSTMs <br> ## Running Locally It is advised to run in [Colab](https://colab.research.google.com/), however, you can also run locally. To do so, first, set up your environment - either through [conda](https://docs.conda.io/en/latest/) or [venv](https://docs.python.org/3/library/venv.html). It is advised to install [PyTorch](https://pytorch.org/get-started/locally/) in advance with GPU acceleration. Then, follow the steps: 1. Install the required packages: ```bash pip install -r requirements.txt ``` 2. Change every first code cell in `.ipynb` files to: ```bash %cd cs231n/datasets/ !bash get_datasets.sh %cd ../../ ``` 3. Change the first code cell in section **Fast Layers** in [ConvolutionalNetworks.ipynb](assignment2/ConvolutionalNetworks.ipynb) to: ```bash %cd cs231n !python setup.py build_ext --inplace %cd .. ``` I've gathered all the requirements for all 3 assignments into one file [requirements.txt](requirements.txt) so there is no need to additionally install the requirements specified under each assignment folder. If you plan to complete [TensorFlow.ipynb](assignment2/TensorFlow.ipynb), then you also need to additionally install [Tensorflow](https://www.tensorflow.org/install). > **Note**: to use MPS acceleration via [Apple Metal](https://developer.apple.com/metal/), see the comment in [#4](https://github.com/mantasu/cs231n/issues/4#issuecomment-1492202538). ## Examples <details><summary><b>Inline question example</b></summary> <br> <b>Inline
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matched fp:4a90863bdca7b80c, topic:deep-learning, topic:pytorch, topic:tensorflow