cs231n and cs231n-convolutional-neural-networks-solutions
One project offers course notes and assignments for Stanford's CS231n, while the other provides assignment solutions for the same course, making them complements for students studying visual recognition.
About cs231n
mirzaim/cs231n
Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
This resource provides comprehensive notes and assignment solutions for Stanford's CS231n course on Convolutional Neural Networks for Visual Recognition. It helps students and practitioners understand and implement various deep learning models for image processing, covering topics from basic classifiers to advanced generative networks and image captioning. It's ideal for those learning or reviewing core concepts in computer vision and deep learning.
About cs231n-convolutional-neural-networks-solutions
madalinabuzau/cs231n-convolutional-neural-networks-solutions
Assignment solutions for the CS231n course taught by Stanford on visual recognition. Spring 2017 solutions are for both deep learning frameworks: TensorFlow and PyTorch.
This provides completed assignments for the Stanford CS231n course on visual recognition, helping students learn to build and train convolutional neural networks. You get structured problem sets and their solutions, which demonstrate how to implement deep learning models using TensorFlow and PyTorch. This is ideal for students or self-learners taking the CS231n course or similar deep learning programs.
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