Dimensionality Reduction using an Autoencoder in Python
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.
Artificial Neural Network
由 RR 提供Jun 12, 2020
I really enjoyed this course. Thank you very much for the valuable teaching.
由 JV 提供Jul 4, 2020
Short and clear. A nice hand-ons introduction to the topic.
由 UI 提供May 3, 2020
Very practical and useful introductory course. Looking for the next courses :)
由 MH 提供Sep 16, 2020
Last two videos is really difficult for me, it will be very helpful if you please include some theories behind thode techniques in the reading section.