Image Compression and Generation using Variational Autoencoders in Python

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In diesem Guided Project werden Sie:

How to preprocess and prepare data for vision tasks using PyTorch

What a variational autoencoder is and how to train one

How to compress, reconstruct, and generate new images using a generative model

Clock90 minutes
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

In this 1-hour long project, you will be introduced to the Variational Autoencoder. We will discuss some basic theory behind this model, and move on to creating a machine learning project based on this architecture. Our data comprises 60.000 characters from a dataset of fonts. We will train a variational autoencoder that will be capable of compressing this character font data from 2500 dimensions down to 32 dimensions. This same model will be able to then reconstruct its original input with high fidelity. The true advantage of the variational autoencoder is its ability to create new outputs that come from distributions that closely follow its training data: we can output characters in brand new fonts. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Kompetenzen, die Sie erwerben werden

Image CompressionMachine LearningVision

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  1. An introduction to the variational autoencoder and our project

  2. Dataset visualization and preprocessing

  3. Dataset split into training and validation sets

  4. U​se data loaders to handle memory overload

  5. Create VAE architecture

  6. Create training loop for VAE

  7. R​esults of our model and short introduction to other potential projects using a VAE

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