L4.3 Vectors, Matrices, and Broadcasting @SebastianRaschka
L4.3 Vectors, Matrices, and Broadcasting  @SebastianRaschka
Uploaded February 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books

Computational libraries such as NumPy and PyTorch extend linear algebra concepts in useful ways to make its usage more convenient in practice. In this video, we will learn about broadcasting, which is a concept for creating implicit dimensions for matrix-vector addition and other operations.

Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L04_linalg-dl_slides.pdf

-------

This video is part of my Introduction of Deep Learning course.

Next video: youtu.be/4pnoymfFiYM

The complete playlist: youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51

A handy overview page with links to the materials: sebastianraschka.com/blog/2021/dl-course.html

-------

If you want to be notified about future videos, please consider subscribing to my channel: youtube.com/c/SebastianRaschka
L4.3 Vectors, Matrices, and BroadcastingL5.2 Relation Between Perceptron and Linear RegressionL10.5.3 (Optional) Dropout Ensemble InterpretationL8.7.1 OneHot Encoding and Multi-category Cross EntropyL4.4 Notational Conventions for Neural NetworksBuild an LLM from Scratch 7: Instruction FinetuningBuild an LLM from Scratch 6: Finetuning for ClassificationL8.7.2 OneHot Encoding and Multi-category Cross Entropy   Code ExampleL15.2 Sequence Modeling with RNNsL18.6: A DCGAN for Generating Face Images in PyTorch   Code ExampleL7.0 GPU resources & Google ColabScaling PyTorch Model Training With Minimal Code Changes
Sebastian Raschka |

L4.3 Vectors, Matrices, and Broadcasting

SHARE TO X SHARE TO REDDIT SHARE TO FACEBOOK WALLPAPER