CS231n and Deep-Learning-Computer-Vision
These are competitors—both are independent collections of assignment solutions for the same Stanford CS231n course, offering alternative implementations that serve the same educational purpose.
About CS231n
jariasf/CS231n
My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition
This collection provides solved assignments for a university course on Convolutional Neural Networks (CNNs) for visual recognition. It offers practical examples of image classification, feature extraction, and neural network implementation. Students learning about deep learning and computer vision would use these solutions to understand core concepts and check their own work.
About Deep-Learning-Computer-Vision
seloufian/Deep-Learning-Computer-Vision
My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
This project provides comprehensive assignment solutions for two leading university courses in deep learning and computer vision: Stanford's CS231n and Michigan's EECS 498-007/598-005. It takes theoretical concepts from lectures and applies them through practical implementations, using Python with NumPy, TensorFlow, and PyTorch. The solutions are designed for machine learning practitioners and researchers who want to deepen their understanding of computer vision algorithms.
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