stanford-cs231n-assignments-2020 and Deep-Learning-Computer-Vision
Both repositories are independent, competing solutions to the assignments for Stanford's CS231n course, serving the same purpose of providing example implementations for students.
About stanford-cs231n-assignments-2020
amanchadha/stanford-cs231n-assignments-2020
This repository contains my solutions to the assignments for Stanford's CS231n "Convolutional Neural Networks for Visual Recognition" (Spring 2020).
This collection of assignments helps you learn to build AI systems that can 'see' and interpret images, similar to how human brains process visual information. You'll work with images and develop models that can classify objects, understand visual styles, or even generate new images. This is ideal for students or engineers new to computer vision and deep learning who want to build foundational skills.
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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