Fundamentals of Artificial Intelligence and Deep Learning Frameworks
My solutions for the course assignments of ENPM809K-Fundamentals of Artificial Intelligence and Deep Learning Frameworks. The course assignments are the same as the Stanford CS231n Assignments. This repository contains Implementions of kNN, Multiclass SVM classifiers with SVM and Softmax loss with techniques like Stochastic Gradient Descent and Cross-validation from scratch for the task of classifying Images from the CIFAR-10 dataset. As well as implementations of Multi-layer Fully Connected Neural Networks with training techniques like Backpropagation, Batch Normalization and, Layer Normalization while using various optimization techniques (SGD, RMSProp, AdaGrad, Adam) for the task of Image Classification on CIFAR-10. PyTorch implementation of Two, Three layer Fully Connected Networks, Convolutional Neural Networks, a VGG-like network for the open-ended challenge.
- Google Colaboratory
- Python 3.10 Programming Language