The course website: http://cs231n.stanford.edu/
Here are my solutions for this above course with some modification to the base code. As of this writing I have yet completed the course material; completed assignments are marked [done!].
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!]