| 1 | To explain the fundamental concepts of deep learning, neural network architectures, and the related mathematical foundations. |
| 2 | To build and train models such as artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). |
| 3 | To develop data-driven projects using deep learning libraries such as TensorFlow and PyTorch. |
| 4 | To analyze the performance of deep learning models and improve them using optimization and regularization techniques. |