Image Noise Reduction with Auto-encoders using TensorFlow に戻る
Coursera Project Network による Image Noise Reduction with Auto-encoders using TensorFlow の受講者のレビューおよびフィードバック
108件の評価
コースについて
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.
人気のレビュー
NL
2020年4月7日
Really great learning for beginners. Through project learning it gives very good confidence. But rhyme desktop should be available until completion of project.
NS
2020年8月15日
nice presentation skill, it is helpful for me to noise reduction and image processing
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