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 yet completed the course material; completed assignments are marked [done!], otherwise they are as originally downloaded from the course site.
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)
- Q3: Dropout (10 points)
- Q4: ConvNet on CIFAR-10 (30 points)
- Assignment #3
- Q1: Image Captioning with Vanilla RNNs (40 points)
- Q2: Image Captioning with LSTMs (35 points)
- Q3: Image Gradients: Saliency maps and Fooling Images (10 points)
- Q4: Image Generation: Classes, Inversion, DeepDream (15 points)
Oh, and you should check out MyHumbleSelf's Assignment Solutions. This guy is a Stanford student, so his answers would likely be what you will get if you enrolled yourself. His is from the previous intake; if you looking for Winter 2016 intake, check out ctheory's assignment solutions.