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Вернуться к Classify Radio Signals from Space using Keras

Отзывы учащихся о курсе Classify Radio Signals from Space using Keras от партнера Coursera Project Network

4.5
звезд
Оценки: 233
Рецензии: 37

О курсе

In this 1-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and use it to solve an image classification problem. The data we are going to use consists of 2D spectrograms of deep space radio signals collected by the Allen Telescope Array at the SETI Institute. We will treat the spectrograms as images to train an image classification model to classify the signals into one of four classes. By the end of the project, you will have built and trained a convolutional neural network from scratch using Keras to classify signals from space. 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. 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....

Лучшие рецензии

SB
23 мая 2020 г.

The explanations were elaborate and insightful. But the choice of hyperparams seemed to be arbitrary and no justification was provided for it.

IK
29 окт. 2020 г.

Great course. Instructor knows the subject well and guides you through the material explaining each part. Thank you

Фильтр по:

1–25 из 37 отзывов о курсе Classify Radio Signals from Space using Keras

автор: tejasva s

13 мая 2020 г.

need more attention to theory behind and working of functions

автор: Praveen K

29 апр. 2020 г.

No explanations from the basics of the imported libraries.

автор: Dr.Ravi K

25 апр. 2020 г.

Some more details can be inserted for more satellite images. Also there should be at least 2-3 different examples in the project for better understanding of the background fundamentals used behind this code.

автор: Sudharsan B

24 мая 2020 г.

The explanations were elaborate and insightful. But the choice of hyperparams seemed to be arbitrary and no justification was provided for it.

автор: JAYDEEP D D

7 июня 2020 г.

IT WAS GREAT EXPERIENCE TO WORK AND PERFORM THIS AMAZING PROJECT WITH SNEHAN KEKRE SIR

автор: Prithviraj P G

4 мая 2020 г.

The course could be more effective if the teaching learning process was simple

автор: Srivishnu. S

8 июля 2020 г.

Excellent course, I really learned a lot by doing the programming part live, while watching the lectures. The sad part was Rhyme could provide me only a little time to use the cloud desktop. Creating a CNN model and seeing it work was amazing. I feel confident that I can apply the same to other data and also tweak some parameters so that it can be understood better.

автор: Isuru K

30 окт. 2020 г.

Great course. Instructor knows the subject well and guides you through the material explaining each part. Thank you

автор: Adarsh M L

19 июля 2020 г.

One of the best guided project ever.

автор: Dipraj C J

12 июня 2020 г.

It was really good project.

автор: Mayank S

11 мая 2020 г.

Thankyou Sir, Well taught.

автор: Gangone R

2 июля 2020 г.

very useful course

автор: SASI V T

12 июля 2020 г.

EXCELLENT

автор: Anitha V

12 июля 2020 г.

EXCELLENT

автор: Santiago G

21 сент. 2020 г.

Thanks

автор: GUNDABATTINA T

22 мая 2020 г.

good..

автор: aithagoni m

6 авг. 2020 г.

good

автор: p s

23 июня 2020 г.

Good

автор: sarithanakkala

23 июня 2020 г.

Good

автор: tale p

22 июня 2020 г.

good

автор: Vajinepalli s s

16 июня 2020 г.

nice

автор: Sanjaysuman S G

13 июня 2020 г.

I have a basic knowledge in Deep Learning , so i was confident that i could learn this Project. It was little difficult but at the end i felt happy that I got try out & learn something interesting from this Project.

автор: RITESH C

10 июня 2020 г.

A very well-structured project. Surely, gave me a wonderful insight into building my own CNN.

However, the cloud platform was lagging and slow. Could have been a better user experience.

автор: Thomas N

4 сент. 2020 г.

Using the Rhyme platform is unstable. Some of the functions are not available for the student. Correcting the way the Rhyme platform jumps around is frustrating.

автор: Sagnik S

14 июня 2020 г.

Good for people who already know the basics of deep learning and can work with CNNs.