Deep Learning for Engineers

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Interest in deep learning based applications has grown exponentially over the past few years, due to its surprising effectiveness. While deep learning may resemble magic, it is simply a collection of algorithms capable of finding useful representations of data, at multiple levels of abstraction.

This course will demystify the fundamentals of contemporary deep learning techniques, covering just enough theory to ground hands-on application. After the course, students should be comfortable tackling their own deep learning projects, whether for their work or as side projects.

Projects and exercises

This course involves a number of hands-on programming exercises, implementing many deep learning components from scratch, as well as utilizing existing tools once we have developed a thorough understanding of them. The course will be in Python, given its high readability and being common in the deep learning field.

Assumed knowledge

Participants are expected to be confident programmers, with a recommended 2 or more years of professional experience or equivalent. All exercises will be in Python. No math background is assumed beyond high school level, although some familiarity with linear algebra is advantageous (you may enjoy watching some of Grant Sanderson’s wonderful Essence of Linear Algebra videos beforehand).