L4.0 Linear Algebra for Deep Learning   Lecture Overview @SebastianRaschka
L4.0 Linear Algebra for Deep Learning   Lecture Overview  @SebastianRaschka
Uploaded February 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books

Linear algebra allows us to express and implement neural networks effectively. In this lecture, we will do a little detour and go over the linear algebra notation and conventions for deep learning.

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/JXfDlgrfOBY

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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L4.0 Linear Algebra for Deep Learning   Lecture OverviewL4.3 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 Colab
Sebastian Raschka |

L4.0 Linear Algebra for Deep Learning -- Lecture Overview

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