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An Introduction to Autoencoders in Deep Learning

5 min readAug 2, 2022

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Photo by Visax on Unsplash

Autoencoders (AEs) are a type of neural network architecture that is able to find a compressed representation of the input data such as image, video, text, speech, etc.

Autoencoders fall under unsupervised learning algorithms as they learn the compressed representation of the data automatically from the input data without labels.

Practical applications of autoencoders

I decided to list down the following applications of autoencoders at the beginning of the article as this will motivate you to learn more about autoencoders!

  • Transform noisy data into clean data using denoising autoencoders (denoising)
  • Add color to grayscale images (image colonization) using automatic colonization autoencoders
  • Increase the resolution of images to improve the details (super-resolution)
  • Compress images to save memory (image compression)
  • Reduce the dimensionality of data (dimensionality reduction)
  • Extract the most important features of the input data (feature extraction)
  • Generate new images with slight variations using…

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Data Science 365
Data Science 365
Rukshan Pramoditha
Rukshan Pramoditha

Written by Rukshan Pramoditha

3,000,000+ Views | BSc in Stats (University of Colombo, Sri Lanka) | Top 50 Data Science, AI/ML Technical Writer on Medium

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