L16.3 Convolutional Autoencoders & Transposed Convolutions @SebastianRaschka
L16.3 Convolutional Autoencoders & Transposed Convolutions  @SebastianRaschka
Uploaded April 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books

Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L16_autoencoder__slides.pdf

0:00 Introduction
0:18 A Convolutional Autoencoder
3:29 Regular Convolution: 33
6:34 Transposed Convolution (3x3 kernel, stride=2)
10:27 Regular Convolution: stride = 1
14:43 Regular Convolution: output

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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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L16.3 Convolutional Autoencoders & Transposed ConvolutionsLLMs: A Journey Through Time and ArchitectureL6.0 Automatic Differentiation in PyTorch   Lecture OverviewL9.0 Multilayer Perceptrons   Lecture OverviewL11.1  Input NormalizationL15.5 Long Short-Term MemoryDeveloping an LLM: Building, Training, FinetuningL6.5 A Closer Look at the PyTorch APIL10.5.4 Dropout in PyTorchL15.1: Different Methods for Working With Text Data13.2 Filter Methods for Feature Selection   Variance Threshold (L13: Feature Selection)LLM Building Blocks & Transformer Alternatives
Sebastian Raschka |

L16.3 Convolutional Autoencoders & Transposed Convolutions

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