Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick | Towards Data Science
A clear, math-first walkthrough of how VAEs learn to generate new data
autoencoders
Variational Autoencoder (VAE) with Discrete Distribution using Gumbel Softm
Theory and PyTorch Implementation
A Deep Dive into Autoencoders and Their Relationship to PCA and SVD
An in-depth exploration of autoencoders and dimensionality reduction
Hands-on Generative AI with GANs using Python: Autoencoders
Start with Autoencoders to better understand GANs
Uncovering Anomalies with Variational Autoencoders (VAE): A Deep Dive into
An example use case of using Variational Autoencoders (VAE) to detect anomalies in all types of data
NSVQ: Improved Vector Quantization technique for Neural Networks Training
Efficient vector quantization for machine learning optimizations (eps. vector quantized variational autoencoders), better than straight…
Introduction to Embedding, Clustering, and Similarity
Introduction to key elements of ML and Autoencoders: Embedding, Clustering, and Similarity.
Sparse Autoencoder Neural Networks — How to Utilise Sparsity for Robust Inf
A comparison between Undercomplete and Sparse AE with a detailed Python example
Autoencoders (AE) — A Smart Way to Process Your Data Using Unsupervised Neural Networks
What is an Autoencoder, and how to build one in Python?
Autoencoders: From Vanilla to Variational
Because GANs are not all you need
scikit-and-tensorflow-workbooks/ch15-autoencoders.ipynb at master · bjpcjp/scikit-and-tensorflow-workbooks
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks
Essential Guide to Auto Encoders in Data Science (Part 2)
Discussing the type of Auto Encoders
variational autoencoders at DuckDuckGo
DuckDuckGo. Privacy, Simplified.
Building a VAE Playground with Streamlit
With Streamlit creating a deploying a web app can be very easy!
Beginner guide to Variational Autoencoders (VAE) with PyTorch Lightning (Part 2)
This blog post is part of a mini-series that talks about the different aspects of building a PyTorch Deep Learning project using Variational Autoencoders. In Part 1, we looked at the variational…
Curious about Variational Autoencoders (VAEs)? Start Here.
In recent years, GANs (generative adversarial networks) have been all the rage in the field of deep-learning generative models, leaving…
Neural Networks 201: All About Autoencoders
Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.