CS231n-2017-Summary and Stanford-CS231n-2021-and-2022
These are competitors: both are educational note-taking resources for the same Stanford CS231n course, serving the same purpose of summarizing course content for learners, with tool A offering broader coverage across the entire 2017 course while tool B provides merged notes from more recent 2021-2022 offerings.
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.
Covers 16 lectures spanning foundational concepts through advanced topics in computer vision and deep learning, including loss functions, CNN architectures (ResNet, VGG), RNNs, object detection, generative models, and adversarial training. The summary integrates practical optimization techniques like backpropagation and gradient descent alongside theoretical foundations, with emphasis on ImageNet-scale classification tasks and hands-on training methodologies for deep neural networks.
About Stanford-CS231n-2021-and-2022
DaizeDong/Stanford-CS231n-2021-and-2022
Notes and slides for Stanford CS231n 2021 & 2022 in English. I merged the contents together to get a better version. Assignments are not included. 斯坦福cs231n的课程笔记(英文版本，不含实验代码)，将2021与2022两年的课程进行了合并，分享以供交流。
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