Introduction to Deep Learning Applications in Engineering

5 credits

Covers neural networks and their applications in engineering. Introduces fundamental deep learning concepts, practical skills, and common applications of neural networks in the areas of data analysis and data generation. Examines the building blocks of neural networks, their training, and basic architectures. Includes a survey of advanced topics and architectures such as generative networks, transformers, and reinforcement learning. Recommended: coursework in Python programming (E E 241); linear algebra (MATH 208); probability and statistics (STAT 390 or E E 416); and machine learning (E E 511).