This course provides an in-depth exploration of neural networks, with a strong focus on deep learning. Students will learn fundamental concepts, including shallow and deep neural networks, optimization techniques, loss functions, and performance evaluation. The course covers key architectures such as Convolutional Neural Networks (CNNs), Transformers, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs). Emphasis is placed on practical applications, with Python-based implementation, assignments, and a hands-on project aimed at solving real-world problems. Students will also engage in critical analysis of recent research in the field.