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CS231n: Deep Learning for Computer Vision

Stanford - Spring 2023

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Overview

These are my solutions for the CS231N course assignments offered by Stanford University (Spring 2023). Written questions are explained in detail, the code is brief and commented.

Main sources (official)

Requirements

These assignments are suggested using Google Colab if you don't have any GPUs. All instructions for setting up a virtual environment are on the Course Notes page.

Note: Python 3.8 or newer should be used

Solutions

Structure

For every assignment, i.e., for directories assigment1 through assignment5, there is coding and written parts. The solutions.pdf files are generated from latex directories where the provided templates were filled while completing the questions in handout.pdf files and the code.

Assignments

  • A1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network
  • A2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch & Network Visualization
  • A3: Network Visualization, Image Captioning with RNNs and Transformers, Generative Adversarial Networks, Self-Supervised Contrastive Learning

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Assignment solutions and notes for CS231N: Deep Learning for Computer Vision - Stanford / Spring 2023

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