L4.4 Notational Conventions for Neural Networks @SebastianRaschka
L4.4 Notational Conventions for Neural Networks  @SebastianRaschka
Uploaded February 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books

While we are free to use any linear algebra notation we like as long as it is correct, there are certain conventions in the deep learning community. In this video, we will go over some of these conventions for computing the values in a forward pass of a neural network.

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

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

Next video: youtu.be/XswEBzNgIYc

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

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L4.4 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 ChangesL8.5 Logistic Regression in PyTorch   Code ExampleL14.1: Convolutions and Padding13.0 Introduction to Feature Selection (L13: Feature Selection)Build A Reasoning Model (From Scratch), Page 198
Sebastian Raschka |

L4.4 Notational Conventions for Neural Networks

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