9 Sep - 3 Oct
$1800Apply for this course
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.
Q: Why did the deep net cross the road?— Sarah Schwettmann (@cogconfluence) January 17, 2016
A: We don't know. But look, it did it really well...#deeplearning
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.
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.
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).
This course will next be taught by
Dr. Brian Spiering is a Professor of Computer Science at University of San Francisco, with a focus on natural language processing and artificial intelligence. At Bradfield, he teaches the Deep Learning for Engineers course. He is also active in the San Francisco tech community as a volunteer and mentor at DataKind SF Bay and Delta Analytics.