CS231n: Convolutional Neural Networks for Visual Recognition - Assignment Solutions

This repository contains my solutions to the selected assignments for Stanford's CS231n "Convolutional Neural Networks for Visual Recognition" course (Spring 2020).

Find course notes and assignments here and be sure to check out the video lectures for Winter 2016 and Spring 2017!

Assignments have been completed using TensorFlow 2.0.

Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network

Q1: k-Nearest Neighbor Classifier

Q2: Training a Support Vector Machine

Q3: Implement a Softmax classifier

Q4: Two-Layer Neural Network

Q5: Higher Level Representations: Image Features

Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets

Q1: Fully-connected Neural Network

Q2: Batch Normalization

Q3: Dropout

Q4: Convolutional Networks

Q5: TensorFlow v2 on CIFAR-10

Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks

Q1: Image Captioning with Vanilla RNNs

Q2: Image Captioning with LSTMs

Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images using TensorFlow v2

Q4: Style Transfer using TensorFlow v2

Q5: Generative Adversarial Networks using TensorFlow v2

Course notes

• Official course notes

GCP:

While Colab is good for assignments, and is still a helpful and free tool for experimentation for your project, you will likely need a dedicated GPU instance when you start training on large datasets. The following link is really helpful for setting up the Google Cloud Platform (GCP). This instructoin is provided by cs231n as well.

GCP setup instruction