L5.0 Gradient Descent   Lecture Overview @SebastianRaschka
L5.0 Gradient Descent   Lecture Overview  @SebastianRaschka
Uploaded February 2021 | Updated September 2026, 1 week ago
Sebastian's books: sebastianraschka.com/books

It's time to learn how neural networks learn. The inarguably most popular learning algorithm for neural networks is backpropagation, which is, in turn, based on gradient descent. In this video, you will get an overview of what the next couple of lectures cover on our journey to understanding gradient descent and backpropagation.

Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L05_gradient-descent_slides.pdf

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

Next video: youtu.be/b4DXHd3RwqA

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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If you want to be notified about future videos, please consider subscribing to my channel: youtube.com/c/SebastianRaschka
L5.0 Gradient Descent   Lecture Overview13.4.2 Feature Permutation Importance (L13: Feature Selection)L10.0 Regularization Methods for Neural Networks   Lecture OverviewL6.3 Automatic Differentiation in PyTorch   Code ExampleL14.3: Architecture OverviewDeep Learning News #7 Mar 13 2021L4.5 A Fully Connected (Linear) Layer in PyTorchL10.3 Early StoppingL5.5 (Optional) Calculus Refresher II: GradientsBuild an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate TextL14.3.1.1 VGG16 OverviewL9.3.2 Multilayer Perceptron in PyTorch   Code Example Part 2/3 (Jupyter Notebook)
Sebastian Raschka |

L5.0 Gradient Descent -- Lecture Overview

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