Вернуться к Deep Learning with PyTorch : Build an AutoEncoder
Отзывы учащихся о курсе Deep Learning with PyTorch : Build an AutoEncoder от партнера Coursera Project Network
О курсе
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 из 3 отзывов о курсе Deep Learning with PyTorch : Build an AutoEncoder
автор: David H
•5 мар. 2021 г.
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.
автор: XZS
•21 мар. 2021 г.
Great Short course. It would be great if I can download my notebook. or revisit the videos after completion.
автор: Bob K
•21 апр. 2021 г.
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
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