BSCS_AIE 481: Deep Learning and Neural Networks

This course provides an in-depth study of neural network architectures and their applications in deep learning. Students will explore the theoretical foundations of neural networks, learn to implement various architectures such as convolutional and recurrent neural networks, and apply these models to real-world tasks like image recognition, natural language processing, and more.

By the end of this course, students will be able to:
1. Explain the architecture and function of different neural network models.
2. Implement deep learning models for tasks such as image classification, object detection,
and language modeling.
3. Use deep learning frameworks to build and train neural networks.
4. Evaluate the performance of deep learning models and fine-tune them for better results.
5. Understand the ethical implications and challenges associated with the deployment of AI
systems.