Derivatives and Differentiation — Segment 2 of Subject 3, "Limits & Derivatives" – ML Foundations

#MLFoundations #Calculus #MachineLearning

In this segment of Calculus videos, we use a combination of color-coded equations, paper-and-pencil exercises, and hands-on Python code demos to deeply understand how differentiation allows us to find derivatives.

More specifically, we’ll cover the Delta Method, the Differentiation Equation, Differentiation Notation, and a handful of nifty rules that enable us to quickly calculate the derivatives of a wide range of functions, including those found throughout machine learning.

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 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.

Видео Derivatives and Differentiation — Segment 2 of Subject 3, "Limits & Derivatives" – ML Foundations канала Jon Krohn