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Learner Reviews & Feedback for Image Classification with CNNs using Keras by Coursera Project Network

527 ratings
80 reviews

About the Course

In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset. 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 (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with python and convolutional neural networks. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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....

Top reviews

Aug 10, 2020

This is a very good guided project.\n\nI thank Amit Yadav and Coursera for his teaching in Image Classification with CNNs using Keras.\n\nThank You

Jun 2, 2020

Really enjoyed learning from Amit Yadav. He promptly answers any query posted in the discussion forum. Looking forward to learning more from him.

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76 - 80 of 80 Reviews for Image Classification with CNNs using Keras

By Arvind K V

May 19, 2020

Very basic

By Mayank S

May 12, 2020

Worst instructor till now on coursera, just useless


Jun 20, 2020

Very bad experience with rhyme console

By Deleted A

Apr 28, 2020

i want to unenroll this

By Praveen K R L

Jul 9, 2020

Worthless project