Chevron Left
Image Denoising Using AutoEncoders in Keras and Python に戻る

Coursera Project Network による Image Denoising Using AutoEncoders in Keras and Python の受講者のレビューおよびフィードバック

4.5
235件の評価
34件のレビュー

コースについて

In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Autoencoders - Import Key libraries, dataset and visualize images - Perform image normalization, pre-processing, and add random noise to images - Build an Autoencoder using Keras with Tensorflow 2.0 as a backend - Compile and fit Autoencoder model to training data - Assess the performance of trained Autoencoder using various KPIs 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....

人気のレビュー

AM

Jul 22, 2020

My Cloud access was denied after a certain time.. I had to do the coding all over again in my notebook. Rest was good.

SK

Jul 11, 2020

Clear explanation of auto encoders. This guided project was just right to get a good understanding of the topic

フィルター:

Image Denoising Using AutoEncoders in Keras and Python : 26 - 34 / 34 レビュー

by Aniket G

May 22, 2020

This project has helped me to build m basic well towards image processing and I would recommend this course to everyone

by sairam g

May 14, 2020

Expected something from this course

but i was dissatisfied

by Abhirami C S

Apr 09, 2020

good course for both beginners and freshers

by Alan P

Apr 11, 2020

great hands on project

by Arpit P

Sep 10, 2020

Best Explaination

by aithagoni m

Jun 10, 2020

nice

by Aditya K S

Jun 02, 2020

I faced a lot of problem in doing the course on the Rhyme platform.

It took very long to load and either the Cloud PC was not working or the video of the instructor.

Maybe it was due to low network bandwidth but still this was a major problem I faced, rest all was good.

by Sabina T

Jun 02, 2020

Good Project. Would like to see more projects using different kinds of Autoencoders.

by Simon S R

Aug 31, 2020

Sadly turned out to be rather disappointing...