This project involved the implementation of a fully connected neural network from the ground up, with the CIFAR10 dataset utilized for model training. Key concepts learned throughout the process included linear regression, forward propagation, backward propagation, and vectorization. Additionally, an extra assignment involved the implementation of Convolutional Neural Networks.

The main purpose of this project is understanding the importance of Vectorization and how it can drastically decrease the processing time for any neural network architecture.