Automatic Differentiation with TensorFlow — Topic 64 of Machine Learning Foundations

#MLFoundations #Calculus #MachineLearning In this video, we use a hands-on code demo in TensorFlow to see AutoDiff in action first-hand, enabling us to compute the derivatives equations instantaneously. 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=PLRDl2inPrW... 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.