Dimensionality Reduction using an Autoencoder in Python

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In this Guided Project, you will:

How to generate and preprocess high-dimensional data

How an autoencoder works, and how to train one in scikit-learn

How to extract the encoder portion from a trained model, and reduce dimensionality of your input data

Clock60 minutes
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively before training a baseline PCA model. You will learn the theory behind the autoencoder, and how to train one in scikit-learn. You will also learn how to extract the encoder portion of it to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics. 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.

Skills you will develop

Dimensionality ReductionArtificial Neural NetworkMachine Learningclustering

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:

  1. An introduction to the problem and a summary of needed imports

  2. Dataset creation and preprocessing

  3. Using PCA as a baseline for model performance

  4. Theory behind the autoencoder architecture and how to train a model in scikit-learn

  5. Reducing dimensionality using the encoder half of an autoencoder within scikit-learn

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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

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