Deep-Learning-CS231

Deep learning code from CS231n (Stanford) Course.

This repository contains the implementation of various concepts surrounding deep learning mainly using numpy.

Python, Numpy and Matplotlib Tutorial : http://cs231n.github.io/python-numpy-tutorial/#numpy

CS231n Assignment Solutions

Completed Assignments for CS231n: Convolutional Neural Networks for Visual Recognition

I have just finished the course online and this repo contains my solutions to the assignments! Enjoyed the assignments the folks at Stanford built to the fullest!

Find course notes and assignments here and be sure to check out video lectures here!

Assignment 1:

• Q1: k-Nearest Neighbor classifier. (Done)
• Q2: Training a Support Vector Machine. (Done)
• Q3: Implement a Softmax classifier. (Done)
• Q4: Two-Layer Neural Network. (Done)
• Q5: Higher Level Representations: Image Features. (Done)

Assignment 2:

• Q1: Fully-connected Neural Network. (Done)
• Q2: Batch Normalization. (Done)
• Q3: Dropout. (Done)
• Q4: Convolutional Networks. (Done)
• Q5: PyTorch / TensorFlow on CIFAR-10. (Done)

Assignment 3:

• Q1: Image Captioning with Vanilla RNNs. (Done)
• Q2: Image Captioning with LSTMs. (Done)
• Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images. (Done)
• Q4: Style Transfer. (Done)
• Q5: Generative Adversarial Networks. (Done)