Image Denoising Using AutoEncoders in Keras and Python

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在此指導項目中,您將:

Understand the theory and intuition behind Autoencoders

Build and train an image denoising autoencoder using Keras with Tensorflow 2.0 as a backend

Assess the performance of trained autoencoders 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 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Autoencoders - Import Key libraries, dataset and visualize images - Perform image normalization, pre-processing, and add random noise to images - Build an Autoencoder using Keras with Tensorflow 2.0 as a backend - Compile and fit Autoencoder model to training data - Assess the performance of trained Autoencoder using various KPIs Note: 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 LearningArtificial Intelligence (AI)Machine LearningPython ProgrammingComputer 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. Project Overview

  2. Import libraries and datasets

  3. Perform data visualization

  4. Perform data preprocessing

  5. Understand the theory and intuition behind autoencoders

  6. Build and train autoencoder model

  7. Evaluate trained model performance

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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