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Image Noise Reduction with Auto-encoders using TensorFlow(으)로 돌아가기

Coursera Project Network의 Image Noise Reduction with Auto-encoders using TensorFlow 학습자 리뷰 및 피드백

4.7
별점
75개의 평가
10개의 리뷰

강좌 소개

In this 2-hour long project-based course, you will learn the basics of image noise reduction with auto-encoders. Auto-encoding is an algorithm to help reduce dimensionality of data with the help of neural networks. It can be used for lossy data compression where the compression is dependent on the given data. This algorithm to reduce dimensionality of data as learned from the data can also be used for reducing noise in data. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed. 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....

최상위 리뷰

필터링 기준:

Image Noise Reduction with Auto-encoders using TensorFlow의 10개 리뷰 중 1~10

교육 기관: Narendra L L

Apr 08, 2020

Really great learning for beginners. Through project learning it gives very good confidence. But rhyme desktop should be available until completion of project.

교육 기관: Ravi P B

Apr 17, 2020

A nice and short project and a good way to built a simple autoencoder and neural network classifier and getting them up and running.

교육 기관: Nilesh N

Mar 28, 2020

Crisp and useful!

교육 기관: XAVIER S M

Jun 02, 2020

Very Helpful !

교육 기관: Sumit Y

Jul 09, 2020

Fine !!

교육 기관: sarithanakkala

Jun 24, 2020

Useful

교육 기관: p s

Jun 23, 2020

Super

교육 기관: tale p

Jun 17, 2020

good

교육 기관: Rohit M

Jun 13, 2020

NICE COURSE :-))

교육 기관: NAIDU P S A

Jun 27, 2020

nice