Image Denoising Using AutoEncoders in Keras and Python

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在此指导 项目中,您将:

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

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. 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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