Chevron Left
返回到 Dimensionality Reduction using an Autoencoder in Python

学生对 Coursera Project Network 提供的 Dimensionality Reduction using an Autoencoder in Python 的评价和反馈

94 个评分


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....



May 3, 2020

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


Jun 12, 2020

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


1 - Dimensionality Reduction using an Autoencoder in Python 的 16 个评论(共 16 个)

创建者 Abhishek P G

Jun 15, 2020

创建者 Felix H

Jun 30, 2020

创建者 Ulvi I

May 4, 2020

创建者 Ramya G R

Jun 13, 2020

创建者 Mayank S

May 4, 2020

创建者 Oscar A C B

Jun 12, 2020

创建者 chandrasekhar u

May 6, 2020

创建者 Gangone R

Jul 2, 2020

创建者 Doss D

Jul 2, 2020

创建者 Sarangan R

Jan 10, 2021

创建者 Joerg A

May 19, 2020

创建者 M H

Sep 17, 2020

创建者 Juan C V

Jul 5, 2020

创建者 Sujeet B

May 7, 2020

创建者 Jorge G

Feb 25, 2021

创建者 Simon S R

Aug 29, 2020