71 ratings
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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
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.
Image Compression
Machine Learning
Vision
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
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by AS
Jun 19, 2020It was really helpful. I am new to PyTorch but it gave a good level of understanding overall. thank you
by AF
Jul 28, 2020It is highly recommended to those who has a basic knowledge in ML and like to start using VAEs in pytorch framework. :-)
by DB
May 28, 2020Good project. Add some more clarity to it , especially to the mathematical background.
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You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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