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Volver a Classify Radio Signals from Space using Keras

Opiniones y comentarios de aprendices correspondientes a Classify Radio Signals from Space using Keras por parte de Rhyme

49 calificaciones
9 revisiones

Acerca del Curso

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....
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1 - 9 de 9 revisiones para Classify Radio Signals from Space using Keras

por Dr.Ravi K

Apr 25, 2020

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.

por Prithviraj P G

May 04, 2020

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

por tejasva s

May 13, 2020

need more attention to theory behind and working of functions

por Praveen K

Apr 29, 2020

No explanations from the basics of the imported libraries.

por Sudharsan B

May 24, 2020

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

por Mayank S

May 11, 2020

Thankyou Sir, Well taught.


May 22, 2020


por Jayesh K T

May 12, 2020

Very nice and cool project. But, more explanation on the project is required.

por hari n s a u

Apr 28, 2020

good in handling the project ,step by step process