This article focuses on giving the readers some basic understanding of the Variational Autoencoders and explaining how they are different from the ordinary autoencoders in Machine Learning and Artificial Intelligence. Unlike vanilla autoencoders(like-sparse autoencoders, de-noising autoencoders .etc), Variational Autoencoders (VAEs) are generative models like GANs ( Generative Adversarial Networks). This article is primarily focused on the Variational Autoencoders and I will be writing soon about the Generative Adversarial Networks in my upcoming posts.In this tutorial, we will be discussing how to train a variational autoencoder(VAE) with Keras(TensorFlow, Python) from scratch. We will be concluding our study with the demonstration of the generative capabilities of a simple VAE.The rest of the content in this tutorial can be classified as the following-

1. Background: Variational AutoEncoders (VAEs)
2. Building VAE in Keras
3. Training VAE on the MNIST dataset
4. Results
5. Image Generation Capabilities
6. Summary
7. Further reading and resources

#deep-learning #data-science #machine-learning #keras