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Unsupervised Learning with Autoencoders

MLCon | Machine Learning Conference via YouTube

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Overview

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This session introduces autoencoders as neural networks that learn from unlabeled data. It covers variants including variational autoencoders and applications such as anomaly detection, denoising, similarity detection, and generation.

Syllabus

Intro
UNSUPERVISED TRAINING
CNN AUTOENCODERS
THE BOTTLENECK
EVEN MORE EXAMPLES
ANOMALY DETECTION
USE CASE 1: ANOMALY EXAMPLES
DENOISING
PRETRAINING
SIMILARITY DETECTION
GENERATIVE AUTOENCODERS
INTRINSIC SPACE & DIMENSION
PAC MAN'S INTRINSIC SPACE
THE IDEAL PAC-MAN BOTTLENECK
BACK IN REALITY...
THE VARIATIONAL AUTOENCODER
ADVANTAGES OF THE VAE
UNSUPERVISED LEARNING AT DIVISIO
SUMMARY COMING UP IN OUR BLOG

Taught by

MLCon | Machine Learning Conference

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