This repository contains code for the Stanford University course on CNNs. Here's the webpage for the course: http://cs231n.github.io/

Note: I have copied each ipython notebook in the assingment to a separate script by the name 'ipynbname_main'. For eg., the complete code for the svm notebook is inside svm_main

Q1: k-Nearest Neighbor classifier (20 points)

Q2: Training a Support Vector Machine (25 points)

Q3: Implement a Softmax classifier (20 points)

Q4: Two-Layer Neural Network (25 points)

Q5: Higher Level Representations: Image Features (10 points)

Q1: Fully-connected Neural Network (30 points)

Q2: Batch Normalization (30 points)

Q3: Dropout (10 points)

Q4: ConvNet on CIFAR-10 (30 points)

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)