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Вернуться к Image Noise Reduction with Auto-encoders using TensorFlow

Отзывы учащихся о курсе Image Noise Reduction with Auto-encoders using TensorFlow от партнера Coursera Project Network

Оценки: 78
Рецензии: 11

О курсе

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....

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1–11 из 11 отзывов о курсе Image Noise Reduction with Auto-encoders using TensorFlow

автор: 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.

автор: 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.

автор: Nilesh N

Mar 28, 2020

Crisp and useful!

автор: XAVIER S M

Jun 02, 2020

Very Helpful !

автор: Sumit Y

Jul 09, 2020

Fine !!

автор: Kamlesh C

Aug 07, 2020


автор: sarithanakkala

Jun 24, 2020


автор: p s

Jun 23, 2020


автор: tale p

Jun 17, 2020


автор: Rohit M

Jun 13, 2020


автор: NAIDU P S A

Jun 27, 2020