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Making an Autoencoder

Towards Data Science

This Article covers how to make an Autoencoder using Keras with Tensorflow 2.0 and the MNIST dataset.

Creating an Autoencoder with PyTorch

Analytics Vidhya

Autoencoders are fundamental to creating simpler representations of a more complex piece of data. They use a famous encoder-decoder architecture that allows for the network to grab key features of…

A Deep Dive into Autoencoders and Their Relationship to PCA and SVD

Towards Data Science

An in-depth exploration of autoencoders and dimensionality reduction Continue reading on Towards Data Science

Machine Learning: Autoencoders

Towards Data Science

I found the simplest definition for an autoencoder through Wikipedia, which translates itself into “A machine learning model that learns a lower-dimensional encoding of data”. This is one of the…

Intro to Autoencoders

TensorFlow Tutorials

This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection. An autoencoder is a special type of neural network that is trained to copy its input to i...

Video: All About Autoencoders

Towards Data Science

Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems — you’ll often find them as components in competition-winning Kaggle submissions as well as…

Implementing under & over autoencoders using PyTorch

Analytics Vidhya

Autoencoder is a neural network which converts data to a more efficient representation in latent space using encoder, and then tries to derive the original data back from the latent space using…

Autoencoder neural networks: what and how?

Towards Data Science

I’ll be walking through the creation of an autoencoder using Keras and Python. First, I’ll address what an autoencoder is and how would we possibly implement one. Then I’ll go through steps of…

Deep inside: Autoencoders

Towards Data Science

Autoencoders (AE) are neural networks that aims to copy their inputs to their outputs. They work by compressing the input into a latent-space representation, and then reconstructing the output from…

Fully Understand AutoEncoder in Deep Learning

Towards AI

Data compression algorithm for artificial intelligence and data science applications Continue reading on Towards AI

Deep-dive into Variational Autoencoders

Analytics Vidhya

In previous posts on autoencoders (Part 1 & Part 2), we explored the intuition, theory and implementation of under and over-autoencoders. The autoencodes have two parts: encoder and decoder. The…

Autoencoders made simple

Towards Data Science

Autoencoders are a type of generative model used for unsupervised learning. Autoencoders learn some latent representation of the image and use that to reconstruct the image. What is this “latent…