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.
Image Compression and Generation using Variational Autoencoders in Python
Taught in English
Instructor: Ari Anastassiou
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Recommended experience
(76 reviews)
What you'll learn
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
Skills you'll practice
Details to know
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Guided Project
Recommended experience
(76 reviews)
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
An introduction to the variational autoencoder and our project
Dataset visualization and preprocessing
Dataset split into training and validation sets
Use data loaders to handle memory overload
Create VAE architecture
Create training loop for VAE
Results of our model and short introduction to other potential projects using a VAE
Recommended experience
Familiarity with machine learning principles is useful. An intermediate level understanding of Python is recommended.
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Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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