Deep Learning 101: Detecting Ships from Satellite Imagery

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在此指导项目中,您将:

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
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

Deep LearningArtificial Intelligence (AI)Machine LearningPython ProgrammingComputer Vision

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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