In this course, students master the most important neural network and deep learning concepts and techniques and start applying them productively in modern artificial intelligence (AI) workplace. Deep learning is the primary technique for data analysis and the solution for many complex problems in natural sciences, linguistics, and engineering. We use deep learning for image classification, manipulation, and generation; speech recognition and synthesis; natural language translation; sound and music manipulation and generation; and navigation of self-driving cars. Students master several key deep learning architectures, such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), variational autoencoders (VAEs), generative adversarial networks (GANs), transformers with attention, and graph neural networks (GNNs). Students master the most essential skills for the efficient use of large language model (LLM)-based applications such as ChatGPT and DALL*E. The course starts with a review of the theoretical foundations of neural networks approach to machine learning including auto-differentiation and back-propagation. The emphasis is on practical applications of deep learning APIs Keras (a package within TensorFlow 2.x framework) and PyTorch.
Credits: 4
View Tuition InformationTerm
Fall Term 2025
Part of Term
Full Term
Format
Flexible Attendance Web Conference
Credit Status
Graduate, Noncredit, Undergraduate
Section Status
Open