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学生对 Coursera Project Network 提供的 Image Noise Reduction with Auto-encoders using TensorFlow 的评价和反馈

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109 个评分

课程概述

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....

热门审阅

NL

Apr 7, 2020

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

NS

Aug 15, 2020

nice presentation skill, it is helpful for me to noise reduction and image processing

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1 - Image Noise Reduction with Auto-encoders using TensorFlow 的 15 个评论(共 15 个)

创建者 Narendra L L

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