The Mathematical Engineering of Deep Learning
Introduction | The Mathematical Engineering of Deep Learning
“In the last few years deep learning has seen explosive growth and even dubbed as the “new electricity”. This is due to its incredible success in transforming and improving a variety of automated applications. At its core, deep learning is a collection of models, algorithms, and techniques, such that when assembled together, efficient automated machine learning is executed. The result is a method to create trained models that are able to detect, classify, translate, create and take part in systems that execute human like tasks and beyond.
In this course we focus on the mathematical engineering aspects of deep learning. For this we survey and investigate the collection of algorithms, models, and methods that allow the statistician, mathematician, or machine learning professional to use deep learning methods effectively. Many machine learning courses focus either on the practical aspects of programming deep learning, or alternatively on the full development of machine learning theory, only presenting deep learning as a special case. In contrast, in this course, we focus directly on deep learning methods, building an understanding of the engineering mathematics that drives this field…”