Deep Learning

This course builds on the artificial neural networks course. Students will be introduced to deep neural networks. Coursework will emphasize convolutional neural networks (CNNs), Sequence Modeling with Recurrent and Recursive Nets, Variational Autoencoders, Deep Generative Models, Representation Learning and Knowledge Transfer, Attention mechanisms and Transformers. Self-supervised learning will also be explored. Students will gain hands-on experience by using Keras and TensorFlow for building and applying deep learning models to image recognition, speech recognition, language translation, and other problems, using real-world datasets.  

Note: Check with the institution regarding start/end dates, prices, and delivery method. These may vary according to program, section, and/or semester.