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Вернуться к Traffic Sign Classification Using Deep Learning in Python/Keras

Отзывы учащихся о курсе Traffic Sign Classification Using Deep Learning in Python/Keras от партнера Coursera Project Network

4.6
звезд
Оценки: 339
Рецензии: 49

О курсе

In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Convolutional Neural Networks (CNNs). - Import Key libraries, dataset and visualize images. - Perform image normalization and convert from color-scaled to gray-scaled images. - Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. - Compile and fit Deep Learning model to training data. - Assess the performance of trained CNN and ensure its generalization using various KPIs. - Improve network performance using regularization techniques such as dropout....

Лучшие рецензии

NB
20 июня 2020 г.

Very nice course, everything was explained perfectly.\n\nCan also add about testing the trained model using external data, like if we want to give an input and perform prediction then how it is done.

FB
21 мая 2020 г.

Instructor was efficient in delivering the knowledge and I understood it very well. The exercises were also great. Overall, my aim for taking this course had been accomplished.

Фильтр по:

26–49 из 49 отзывов о курсе Traffic Sign Classification Using Deep Learning in Python/Keras

автор: Santiago G

1 сент. 2020 г.

Thanks!

автор: Kamlesh C

30 июня 2020 г.

thanks

автор: Arpit S

22 июля 2020 г.

great

автор: tale p

28 июня 2020 г.

good

автор: GIJI S

21 июня 2020 г.

Good

автор: Vajinepalli s s

16 июня 2020 г.

nice

автор: SAMBATURU V

20 апр. 2020 г.

good

автор: Naveen C

14 мая 2020 г.

.

автор: Pranshu N

13 июля 2020 г.

Course misses detailed explanation but its good for those who have just learned CNN's and want a quick hands on experience.

автор: Grace G N B

10 июля 2020 г.

Very interesting topic and best mentor Ryan Ahmed.Thank you very much Sir also Coursera

автор: MAYUR K

9 нояб. 2020 г.

Course was really good but it can have some more stuff like using model in a web app

автор: VINAYAK S

10 мая 2020 г.

It was an amazing experience for me to learn something different.

автор: Parul J (

8 июня 2020 г.

Please allow me to download the code that I wrote on RHYME.

автор: Vineet K

27 апр. 2020 г.

Excellent course to understand how to build CNN and use it.

автор: Aafaq I

13 авг. 2020 г.

Pretty cool idea and made it easy to walk through to code

автор: Tsering W S

24 июля 2020 г.

Nice and well explained project. Thank you

автор: Sarvagya K

15 мая 2020 г.

Good for understanding basics.

автор: MOHAMMED K

13 сент. 2020 г.

Great explaination

автор: Rahil J

31 мая 2020 г.

It's a good guided project. Don't trust Rhyme, the cloud desktop which is being used. I can't practice simultaneously.

PS: There are no datasets available, so check Kaggle

автор: G S

3 июня 2020 г.

Resources might have been provided as the could desktop was not function properly and there was no proper response from instructor for messages

автор: Gurpreet S N

1 июня 2020 г.

I feel few more details can be added to the course especially while explaining CNN

автор: Aritra S

31 мая 2020 г.

The external tool is not good. It is very slow and not user-friendly.

автор: KUNAL S

26 авг. 2020 г.

There is no support on the discussion forums and the dataset is also wrong. Poorly designed and its all spoon fed. There is no use of wasting time on this. It is a useless course because you will not learn anything from it.

автор: raghu r m

10 мая 2020 г.

not completely explaining the methods being used.