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Coursera Project Network による Classification of COVID19 using Chest X-ray Images in Keras の受講者のレビューおよびフィードバック

4.6
32件の評価

コースについて

In this 1 hour long project-based course, you will learn to build and train a convolutional neural network in Keras with TensorFlow as backend from scratch to classify patients as infected with COVID or not using their chest x-ray images. Our goal is to create an image classifier with Tensorflow by implementing a CNN to differentiate between chest x rays images with a COVID 19 infections versus without. The dataset contains the lungs X-ray images of both groups.We will be carrying out the entire project on the Google Colab environment. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for educational purposes. By the end of this project, you will be able to build and train the convolutional neural network using Keras with TensorFlow as a backend. You will also be able to perform data visualization. Additionally, you will also be able to use the model to make predictions on new data. You should be familiar with the Python Programming language and you should have a theoretical understanding of Convolutional Neural Networks. You will need a free Gmail account to complete this project. 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

SP

2021年11月21日

I liked the course as it gives the complete picture of how to make prediction using convolutional neural network. Discussion forum is not so active is a negative point.

HM

2020年11月20日

It was a very good and practical project.

The subject was completely related to the current situation and I really liked this project.

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Classification of COVID19 using Chest X-ray Images in Keras: 1 - 9 / 9 レビュー

by Hamed M

2020年11月21日

by sunita p

2021年11月22日

by Maryam G

2021年8月10日

by Mann B

2021年4月11日

by Muhammad M

2020年12月26日

by Sixto R

2021年5月19日

by NG, S L

2021年2月20日

by Lorrain G S

2021年7月6日

by Aniket Y

2021年8月27日