Pretrained Models for Image Classification
Implemented multiple deep learning architectures — LeNet-5, ResNet-50, Inception, and Xception — for image classification tasks on MNIST and CIFAR-10 datasets. Applied transfer learning and hyperparameter optimization to improve accuracy and generalization. Performed comprehensive model evaluation using precision, recall, F1-score, and confusion matrices, ensuring robust benchmarking across architectures.