mantasu/cs231n
Student solutions for Stanford CS231n course assignments covering convolutional neural networks, image captioning with RNNs and Transformers, self-supervised learning, and Denoising Diffusion Probabilistic Models.
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This repository provides concise solutions to Stanford’s CS231n visual recognition course assignments from 2021-2025. Assignments cover fundamental deep learning topics including k-NN classifiers, softmax, two-layer networks, convolutional architectures, BatchNorm, Dropout, and PyTorch implementations on CIFAR-10. More advanced assignments include image captioning with Vanilla RNNs and Transformers, self-supervised learning for image classification, and Denoising Diffusion Probabilistic Models (DDPM).
Frequently asked
- What is mantasu/cs231n?
- Student solutions for Stanford CS231n course assignments covering convolutional neural networks, image captioning with RNNs and Transformers, self-supervised learning, and Denoising Diffusion Probabilistic Models.
- Is cs231n open source?
- Yes — mantasu/cs231n is an open-source project tracked on heatdrop.
- What language is cs231n written in?
- mantasu/cs231n is primarily written in Jupyter Notebook.
- How popular is cs231n?
- mantasu/cs231n has 483 stars on GitHub.
- Where can I find cs231n?
- mantasu/cs231n is on GitHub at https://github.com/mantasu/cs231n.