Back to Deep Learning with PyTorch : Build an AutoEncoder
Learner Reviews & Feedback for Deep Learning with PyTorch : Build an AutoEncoder by Coursera Project Network
12 ratings
About the Course
In these one hour project-based course, you will learn to implement autoencoder using PyTorch. An autoencoder is a type of neural network that learns to copy its input to its output. In autoencoder, encoder encodes the image into compressed representation, and the decoder decodes the representation to reconstruct the image. We will use autoencoder for denoising hand written digits using a deep learning framework like pytorch.
This guided project is for learners who want to use pytorch for building deep learning models.Learners who want to apply autoencoder practically using PyTorch. In order to be successful in this project, you should be familiar with python , basic pytorch like creating or defining neural network and convolutional neural network.
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1 - 3 of 3 Reviews for Deep Learning with PyTorch : Build an AutoEncoder
By Bob K
•Apr 21, 2021
You're on a 1 hour timer to complete the practical part of the course but it's not made clear. The content was fine but the Rhyme (cloud) service is slow
By XZS
•Mar 21, 2021
Great Short course. It would be great if I can download my notebook. or revisit the videos after completion.
By David L H
•Mar 5, 2021
This is the first of these project-oriented courses that I've taken. I like it very much more than the lecture-quiz format. Granted, I did not learn everything that was presented in the course, but now I know what to study to gain a better understanding of the core material.
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