Description:
Deep learning technologies have matured to the point where all system engineers must be aware of their capabilities and limitations.
Deep learning has demonstrated spectacular success in computer vision, timeseries forecasting, text/language analysis,
and autonomous robots.
This course uses the TensorFlow/Keras development framework
to provide a handson introduction to deep learning. The course topics are learningbyexample problem statement, generalization (statistical approach),
perceptrons (linear models), multilayer perceptrons (MLP), training pipelines, convolution models for computer vision, natural language processing
(recurrent neural networks and transformers), Generative learning (variational autoencoders, generative pretrained transformers, diffusion models),
reinforcement learning (deeq Q networks and adaptivecritic methods), privacy and fairness in machine learning.
