Calculus I: Limits & Derivatives — Subject 3 of Machine Learning Foundations
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In this third subject of Machine Learning Foundations, we’ll use differentiation, including powerful automatic differentiation algorithms, to learn how to optimize learning algorithms. We’ll start with an introduction on what calculus is and learn what limits are in order to understand differentiation from first principles, primarily through the use of hands-on code demos in Python.
There are eight subjects covered comprehensively in the ML Foundations series and this video is from the third subject, "Calculus I: Limits & Derivatives". More detail about the series and all of the associated open-source code is available at github.com/jonkrohn/ML-foundations
The playlist for the Calculus subjects is here: • Calculus for Machine Learning
Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and leading industry conferences, as well as online via O'Reilly, his YouTube channel, and the SuperDataScience podcast.
More courses and content from Jon can be found at jonkrohn.com.