CS231n-2017-Summary and cs231n
These are complements: the summary provides high-level course concepts and notes while the assignments repository offers hands-on coding exercises and solutions, together covering both theoretical understanding and practical implementation of CS231n material.
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 cs231n
mirzaim/cs231n
Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work