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Opiniones y comentarios de aprendices correspondientes a Image Classification with CNNs using Keras por parte de Coursera Project Network

517 calificaciones
78 reseña

Acerca del Curso

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....

Principales reseñas

10 de ago. de 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

2 de jun. de 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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51 - 75 de 79 revisiones para Image Classification with CNNs using Keras

por Ankit G

11 de may. de 2020



2 de may. de 2020


por Rakesh k

24 de may. de 2020


por Chintoo K

16 de sep. de 2020

Good job by the instructor. But for a beginner to choose a particular dataset and performing image classification during preprocessing becomes a cumbersome task. CiFAR 10 dataset is already available in tf.keras's load_data module but using cv2 to read images and assigning label for any real world dataset would be more appreciable .

por Wittaya P

3 de jun. de 2020

I understand that it is quite hard to explain how to code these scripts since the first task, it is not suitable for the Python and CNN beginner at all. However, the color code (red and green) is not explained deeper in more details, so that the output photos with colored texts are different from they should be.

por Marcos R A

28 de jun. de 2020

For me the project was what I expected for a 1-hour project, but it would be great if it could be performed without the virtual machine, it's quite problematic and I couldn't type some symbols (= or []) with the common combinations of my keyboard. Besides this, I really enjoyed this mini project.

por Wan M A B M N

22 de oct. de 2020

The tutorial was good but it would be great if theory is also being explain in details during the tutorials. This could help people to relate to the theory they learn and at the same time get to apply it during tutorials.


19 de may. de 2020

it was short n up to the mark, fully hands on and i came to know many new terms and their working. as it is a 1 hr assignment, this is just sufficient and satisfactory for beginners.

por Mazhar M

30 de sep. de 2020

Rhyme interface is very bad and slow,

I would not pay for courses on rhyme if it were not sponsored by my college

Very good content though for a begginer

por Bhanu P M

20 de jul. de 2020

Perfect project for polishing you deep learning skills mainly CNN and getting introduced to using dataset available globally.


5 de jun. de 2020

The project is good and the instructor explains the main areas of the code very well.

por Shashank J

6 de jun. de 2020

Course was good but rhyme interface was bad and needs an improvement

por Monica S A

29 de may. de 2020

it is very useful for my career development. I like this very much

por Vedant A K

9 de may. de 2020

Not in depth, but still good for understanding


19 de abr. de 2020

Thank you...this course too helpful to me

por Om P

7 de may. de 2020

Not in much details, still worth a try.

por Kuldeep S S

3 de may. de 2020

Good Project for Biggener

por Md. M H

30 de may. de 2020

so helpfull


7 de ago. de 2020


por Pravendra S

29 de jun. de 2020


por Krishan A

12 de may. de 2020



3 de may. de 2020


por Pragati

4 de jun. de 2020

Please, explain the structure of the data used and also the libraries used before starting the actual project.

por Baliram C

12 de jul. de 2020

If you do not know the theory about CNN its nothing at all cause code is found everywhere.

por Arvind K V

19 de may. de 2020

Very basic