Project: Classify Radio Signals from Space using Keras

от партнера
Rhyme
В этом проект с консультациями вы:

Build and train a convolutional neural network (CNN) using Keras

Display results and plot 2D spectrograms with Python in Jupyter Notebook

Clock1.5 hours
IntermediateУчащийся среднего уровня
CloudЗагрузка не требуется
VideoВидео на разделенном экране
Comment DotsАнглийский
LaptopDesktop only

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.

Навыки, которые вы получите

Deep LearningConvolutional Neural NetworkMachine LearningTensorflowkeras

Будете учиться пошагово

На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:

  1. Introduction and Import Libraries

  2. Load and Preprocess SETI Data

  3. Create Training and Validation Data Generators

  4. Build the CNN Model

  5. Learning Rate Scheduling and Compile the Model

  6. Train the Model

  7. Evaluate the Model

Как устроены проекты с консультациями

Ваше рабочее пространство — это виртуальный рабочий стол в браузере. Ничего не нужно загружать.

На разделенном экране видео преподаватель предоставляет пошаговые

Часто задаваемые вопросы

Часто задаваемые вопросы

  • By purchasing a guided project, you'll get everything you need to complete the guided project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

  • Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, guided projects are not available on your mobile device.

  • Guided project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

  • You can download and keep any of your created files from the guided project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

  • Financial aid is not available for guided projects.

  • Auditing is not available for guided projects.

  • At the top of the page, you can press on the experience level for this guided project to view any knowledge prerequisites. For every level of guided project, your instructor will walk you through step-by-step.

  • Yes, everything you need to complete your guided project will be available in a cloud desktop that is available in your browser.

  • You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.