Deep Learning

Catalog description

The course blends deep learning with foundational theory and hands-on practice. Students learn skills needed to design, implement, and critically evaluate modern neural networks. The course progresses through cutting-edge architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Generative Adversarial Networks (GANs), while addressing real-world challenges such as overfitting, bias, and computational efficiency.