Classify Radio Signals from Space using Keras
6730 ya inscrito
6730 ya inscrito
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.
Convolutional Neural Network
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
por JD6 de jun. de 2020
IT WAS GREAT EXPERIENCE TO WORK AND PERFORM THIS AMAZING PROJECT WITH SNEHAN KEKRE SIR
por JT11 de may. de 2020
Very nice and cool project. But, more explanation on the project is required.
por RC9 de jun. de 2020
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.
por TN3 de sep. de 2020
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.