Project: Classify Radio Signals from Space using Keras

提供方
Rhyme
在此指導項目中,您將:

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

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

Clock1.5 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English) + subtitles
Laptop不適用於移動設備

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.

您要培養的技能

Deep LearningConvolutional Neural NetworkMachine LearningTensorflowkeras

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的講師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的虛擬桌面,無需下載。

在分屏視頻中,您的講師會為您提供分步指導

常見問題

常見問題

  • 購買項目後,您將獲得完成項目所需的一切內容,包括通過 Web 瀏覽器訪問云桌面工作空間,其中包含您需要了解的文件和軟件,以及特定領域的專家提供的分步視頻說明。

  • 因為您的工作空間包含適合筆記本電腦或台式計算機使用的雲桌面,所以項目不在移動設備上使用。

  • 項目講師是特定領域的專家,他們在項目的技能、工具或領域上都很有經驗,並且熱衷於分享自己的知識以影響全球數百萬的學生。

  • 您可以從項目中下載並保留您創建的任何文件。為此,您可以在訪問云桌面時使用‘文件瀏覽器’功能。

  • 項目沒有助學金。

  • 您不需要任何前期經驗即可開始項目。講師將逐步指導您完成項目。

  • 是,您可以在瀏覽器的雲桌面中獲得完成項目所需的一切。

  • 您可以通過直接在瀏覽器中的分屏環境中完成項目來進行學習。在屏幕的左側,您將在工作空間中完成任務。在屏幕的右側,您將看到有講師逐步指導您完成項目。