Diabetic Retinopathy Detection with Artificial Intelligence

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Coursera Project Network
在此指導 項目中,您將:

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

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

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

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

In this project, we will train deep neural network model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect the type of Diabetic Retinopathy from images. Diabetic Retinopathy is the leading cause of blindness in the working-age population of the developed world and estimated to affect over 347 million people worldwide. Diabetic Retinopathy is disease that results from complication of type 1 & 2 diabetes and can develop if blood sugar levels are left uncontrolled for a prolonged period of time. With the power of Artificial Intelligence and Deep Learning, doctors will be able to detect blindness before it occurs.

您要培養的技能

Deep LearningMachine LearningPython ProgrammingArtificial Intelligence(AI)Computer Vision

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,您的授課教師將指導您完成每個步驟:

  1. Understand the Problem Statement and Business Case

  2. Import Libraries and Datasets

  3. Perform Data Exploration and Visualization

  4. Perform Data Augmentation and Create Data Generator

  5. Understand the Theory and Intuition Behind Convolutional Neural Networks

  6. Build a ResNet Deep Neural Network Model

  7. Compile and Train the Deep Neural Network Model 

  8. Assess the Performance of the Trained Model

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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