Explore the implementation of autoencoders in Python and Keras, focusing on building the decoder component. Learn to update the build method, construct the decoder architecture, add input layers, dense layers, reshape layers, and convolutional transpose layers. Discover how to create the output layer, recap the decoder construction process, and update the summary method. Gain insights into autoencoder instantiation and architecture summarization. Follow along with code examples and practical demonstrations to enhance your understanding of autoencoder implementation techniques.
How to Implement Autoencoders in Python and Keras - The Decoder
Valerio Velardo - The Sound of AI via YouTube
Overview
Syllabus
Intro
Build method update
Build decoder
Add decoder input
Add dense layer
Add reshape layer
Add convolutional transpose layers
Add output layer
Build decoder recap
Summary method update
Autoencoder instantiation + architecture summary
What's up next?
Taught by
Valerio Velardo - The Sound of AI
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