The MNIST dataset contains grayscale images of handwritten digits, each of size 28x28 pixels. The task is to classify these images into one of 10 categories (digits 0-9). In this project: The dataset ...

Graph Convolutional Networks (GCN) and their variants utilize learnable weight matrices and nonlinear activation functions to extract features from data. The selection of activation functions and ...

Often neural network models having a high classification accuracy on the training data suffers a dent when deployed in real-world scenarios which may be attributed to overfitting. Overfitting occurs ...

The MNIST dataset contains grayscale images of handwritten digits, each of size 28x28 pixels. The task is to classify these images into one of 10 categories (digits 0-9). In this project: The dataset ...