Dimensionality Reduction using an Autoencoder in Python

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En este proyecto guiado, tú:

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
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

  • Dimensionality Reduction
  • Artificial Neural Network
  • Machine Learning
  • clustering

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Instructor

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