This project was completed as a part of the Honors portion of the Convolutional Neural Networks Course on Coursera.
Credit to DeepLearning.AI and the Coursera platform for providing the course materials and guidance.
The main goal of this project is to construct an extensive convolutional network using Residual Networks (ResNets). ResNets allow us to train much deeper networks than was previously feasible, making them ideal for representing complex functions. Throughout the assignment, we will implement ResNet building blocks in a deep neural network using Keras and train a state-of-the-art neural network for image classification. The incorporation of skip connections in the network will enhance its efficiency and performance. With Keras as our primary tool, we will tackle complex image classification tasks effectively.
