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Volver a Dimensionality Reduction using an Autoencoder in Python

Opiniones y comentarios de aprendices correspondientes a Dimensionality Reduction using an Autoencoder in Python por parte de Coursera Project Network

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Acerca del Curso

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....

Principales reseñas

UI

3 de may. de 2020

Very practical and useful introductory course. Looking for the next courses :)

RR

12 de jun. de 2020

I really enjoyed this course. Thank you very much for the valuable teaching.

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1 - 16 de 16 revisiones para Dimensionality Reduction using an Autoencoder in Python

por Abhishek P G

15 de jun. de 2020

por Felix H

30 de jun. de 2020

por Ulvi I

4 de may. de 2020

por Ramya G R

13 de jun. de 2020

por Mayank S

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por Oscar A C B

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por chandrasekhar u

6 de may. de 2020

por Gangone R

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por Doss D

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por Sarangan R

10 de ene. de 2021

por Joerg A

19 de may. de 2020

por M H

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por Juan C V

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por Sujeet B

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por Jorge G

25 de feb. de 2021

por Simon S R

29 de ago. de 2020