The Line Equation as a Tensor Graph — Topic 65 of Machine Learning Foundations
#MLFoundations #Calculus #MachineLearning
In this video, we get ourselves set up for applying Automatic Differentiation within a Machine Learning loop by first discussing how to represent an equation as a Tensor Graph and then actually creating that graph in Python code using the PyTorch library.
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 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.