Deep Learning

Catalog description

This course offers a practical introduction to the principles and techniques of deep learning. Students will learn core topics such as linear and nonlinear neural networks, optimization algorithms, convolutional and recurrent architectures, and regularization methods. Emphasis is placed on building intuition through hands-on programming assignments and projects. By the end of the course, students will be able to design, implement, and evaluate deep learning models for a variety of real-world tasks. The course blends theory with practical coding exercises using Python and PyTorch, enabling students to build, train, and evaluate deep learning models for real-world applications. DASC 6301 , DASC 6303 or consent of the instructor or academic advisor.