L12.0: Improving Gradient Descent-based Optimization   Lecture Overview @SebastianRaschka
L12.0: Improving Gradient Descent-based Optimization   Lecture Overview  @SebastianRaschka
Uploaded March 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books

Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L12_optim__slides.pdf

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

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

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L12.0: Improving Gradient Descent-based Optimization   Lecture OverviewL13.3 Convolutional Neural Network BasicsL8.3 Logistic Regression Loss Derivative and TrainingL12.6 Additional Topics and Research on Optimization AlgorithmsL11.3 BatchNorm in PyTorch   Code ExampleL16.2 A Fully-Connected AutoencoderL16.0 Introduction to Autoencoders   Lecture OverviewL19.4.3 Multi-Head AttentionL14.4.2 All-Convolutional Network in PyTorch   Code ExampleL3.1 About Brains and NeuronsDeep Learning News #8 Mar 20 2021L19.5.2.4 GPT-v2: Language Models are Unsupervised Multitask Learners
Sebastian Raschka |

L12.0: Improving Gradient Descent-based Optimization -- Lecture Overview

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