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1
Intro:
2
Dimensionality reduction
3
Denoising autoencoders
4
Variational autoencoders
5
Training autoencoders
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Introduction
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Generative models
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Variational autoencoders
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Dataset of images
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Denoising autoencoders
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Linear methods
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A friendly introduction to deep learning and neural networks
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Mapping the real numbers to the interval 0,1
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Sigmoid function
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Perceptron
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Correct noise
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Autoencoders as generators
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Latent space
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Training a neural network - loss function
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Training an autoencoder
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Training autoencoders
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Reconstruction loss Mean squared error
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Reconstruction loss log-loss
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Training a variational auto encoder
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the powerful world of autoencoders in this 32-minute video lecture. Dive into dimensionality reduction, denoising autoencoders, and variational autoencoders. Learn about generative models, dataset handling, and linear methods. Gain insights into deep learning and neural networks, including perceptrons and sigmoid functions. Discover how autoencoders function as generators and explore the concept of latent space. Master the training process for neural networks and autoencoders, focusing on loss functions and reconstruction techniques. Access a GitHub repository for hands-on practice and explore recommended videos on related topics such as generative adversarial networks and recurrent neural networks to enhance your understanding of machine learning concepts.

Denoising and Variational Autoencoders

Serrano.Academy
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