Deep Learning 101: Detecting Ships from Satellite Imagery

Offered By
Coursera Project Network
In this Guided Project, you will:

Understand the theory and intuition behind Deep Neural Networks and Convolutional Neural Networks (CNNs)

Build a deep learning model based on Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend

Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.

Clock1.5 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this project, we will train a deep learning model based on Convolutional Neural Networks (CNNs) to detect ships in the satellite images. Satellite imagery are critical in many applications such as defense, agriculture, surveillance and intelligence. This project aims at detecting large vessels (ships) in sea from satellite images using Artificial Intelligence. This project is an introductory project for beginners in deep learning and computer vision.

Skills you will develop

Deep LearningArtificial Intelligence (AI)Machine LearningPython ProgrammingComputer Vision

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the Problem Statement and Business Case

  2. Import Libraries and Datasets

  3. Perform Data Visualization

  4. Perform Image Augmentation

  5. Understand the Theory and Intuition Behind Deep Neural Networks

  6. Build a Deep Neural Network

  7. Train a Deep Neural Network

  8. Assess Trained Network Performance

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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