• Home
  • Fresh Content
  • Courses
  • Resources
  • Podcast
  • Talks
  • Publications
  • Sponsorship
  • Testimonials
  • Contact
  • Menu

Jon Krohn

  • Home
  • Fresh Content
  • Courses
  • Resources
  • Podcast
  • Talks
  • Publications
  • Sponsorship
  • Testimonials
  • Contact
Jon Krohn

Automatic Differentiation – Segment 3 of Subject 3, "Limits & Derivatives" – Machine Learning Foundations

Added on July 28, 2021 by Jon Krohn.

Automatic Differentiation is a computational technique that allows us to move beyond calculating derivatives by hand and scale up the calculation of derivatives to the massive scales that are common in machine learning.

The YouTube videos in this segment, which we'll release every Wednesday, introduce AutoDiff in the two most important Python AutoDiff libraries: PyTorch and TensorFlow.

My growing "Calculus for ML" course is available on YouTube here.

More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.


In Calculus, Data Science, ML Foundations, Professional Development, YouTube Tags machinelearning, datascience, calculus, mathematics, python, pytorch, tensorflow
← Newer: The World is Awful (and it’s Never Been Better) Older: R in Production →
Back to Top