In this course, you will learn the fundamental principles of autograd by building your very own automatic differentiation system completely from scratch. This course is Part 1 in the Flamethrower Core series. This system is based on the popular autograd library, and you'll learn how popular deep learning frameworks like PyTorch can compute gradients and derivatives and perform backpropagation based on any function you give them. As each of these concepts is introduced, you'll implement them in your library and test them out on real world data. View the entire course syllabus below, along with preview lessons. Be sure to click the drop down arrow to see the syllabus in its entirety.

Course Curriculum

  Course Introduction
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  Why Do We Care So Much About Neural Networks?
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  Automatic Differentiation
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  Resources, References, and Course Credits
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