My own code of cs231n: convolutional net for computer vision.The course website: http://cs231n.stanford.edu/ Here are my solutions for this above course (Winter 2016), for the benefit of people who struggle greatly to solve them (like myself). I myself (and some of my coursemates) did not enrolled in Stanford to take this course; I just have generous access to the course notes, lecture videos and assignment code, which is made public for everyone. Therefore I do not guarantee that my solutions are correct, so if you spot any errors do let me know.
As of this writing I have completed the course material
Assignment list:
Assignment #1
Q1: k-Nearest Neighbor classifier (20 points) [done!]
Q2: Training a Support Vector Machine (25 points) [done!]
Q3: Implement a Softmax classifier (20 points) [done!]
Q4: Two-Layer Neural Network (25 points) [done!]
Q5: Higher Level Representations: Image Features (10 points) [done!]
Assignment #2
Q1: Fully-connected Neural Network (30 points) [done!]
Q2: Batch Normalization (30 points) [done!]
Q3: Dropout (10 points) [done!]
Q4: ConvNet on CIFAR-10 (30 points) [done!]
Assignment #3
Q1: Image Captioning with Vanilla RNNs (40 points) [done!]
Q2: Image Captioning with LSTMs (35 points) [done!]
Q3: Image Gradients: Saliency maps and Fooling Images (10 points) [done!]
Q4: Image Generation: Classes, Inversion, DeepDream (15 points) [done!]
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