stanford-cs231 and Deep-Learning-Computer-Vision
About stanford-cs231
machinelearningnanodegree/stanford-cs231
Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).
This project provides a curated set of resources to help you learn about Convolutional Neural Networks for visual recognition. It brings together course materials like lectures, assignments, and notes from Stanford's CS231n, along with supplementary blogs and articles. It's for students enrolled in Udacity's Machine Learning Engineer Nanodegree or anyone looking to deepen their understanding of how computers 'see' and interpret images.
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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work