CS231n

I have been following the course CS231n: Convolutional Neural Networks for Visual Recognition offered by Stanford. This repository maintains the solutions to CS23N assignments.

Course Lectures: Youtube
Course Site: CS231n
A brief description of the assignments:

Assignment 1

1. KNN

• In this assignment, I implemented the K-Nearest Neighbour Algorithm from scratch using vectorised code, applying it on the CIFAR-10 dataset. This was also helpful in understanding basic Image Classification pipeline, cross-validation

2. SVM

• This assignment required me to implement a Multiclass Support Vector Machine (SVM) classifier. It also required me to write from scratch a code to implement SGD (Stochastic Gradient Descent) to optimise the loss function, helped me understand how to claculate the analytical gradient for vector equations.

3. Softmax

• Here, I was required to implement a softmax classifier. Like the SVM assignment, this also required using analytical gradients and calculation of loss functions- something which helped clearsome fundamentals.

4. Two Layer Neural Network

• Built a two layer neural network from scratch by using modularised functions and classes.
• Implemented different layers like: affine, relu, vectorised loss functions for softmax and svm losses.
• Optimised using vanilla SGD.
• Effectively used computational graphs to understand gradient flow; implemented forward and backward passes.
• Tuning of hyperparameters using grid search.