#MLFoundations #Calculus #MachineLearning
In preceding videos in this series, we learned all the most essential differential calculus theory needed for machine learning. In this epic video, it all comes together to enable us to perform machine learning from first principles and fit a line to data points. To make learning interactive and intuitive, this video focuses on hands-on code demos featuring the Python library PyTorch.
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: youtube.com/playlist?list=PLRDl2inPrWQVu2OvnTvtkRpJ-wz-URMJx
Jon Krohn is Chief Data Scientist at the machine learning company Nebula. 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.
