CS231n-2017-Summary and CS231
About CS231n-2017-Summary
mbadry1/CS231n-2017-Summary
After watching all the videos of the famous Standford's CS231n course that took place in 2017, i decided to take summary of the whole course to help me to remember and to anyone who would like to know about it. I've skipped some contents in some lectures as it wasn't important to me.
This document summarizes the key concepts from Stanford's CS231n 2017 course on Convolutional Neural Networks for Visual Recognition. It provides an overview of image classification, neural networks, loss functions, and optimization techniques. This resource is ideal for anyone looking for a condensed explanation of deep learning fundamentals applied to computer vision tasks, particularly those who have viewed the lectures and want a review.
About CS231
MahanFathi/CS231
Complete Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
This project contains completed solutions to programming assignments from the CS231n course on Convolutional Neural Networks. It takes theoretical concepts of deep learning for visual recognition and demonstrates their practical implementation. This is designed for students learning about deep learning and computer vision who want to check their understanding or see working examples.
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