How Generative Adversarial Networks (GANs) learn, theory and application — Introduction In my previous article I talked about generative methods in general and what makes generator networks so powerful. In this article, I want to go more in-depth into the inner workings of Generative Adversarial Networks (GANs). To make sure you understand them fully, I will go through the original GANs…

An in-depth explanation of how CTGANs work — The purpose of this article is to fill the gap in the internet for a good explanation of how Conditional Tabular GANs work. …

An introduction to VAEs, GANs and their challenges — Introduction Training generative models is a lot harder than training classifiers or regressors. When training a classifier in a supervised learning problem, we know the optimal mapping between the inputs and the outputs of our training examples. However, training generative models involves optimizing criteria that is intractable. In this article, I…

Generating Image Data with Variational Auto Encoders — Introduction Variational Auto-Encoders (VAEs) are an amazing machine learning architecture. They allow us to approximate high-dimensional latent spaces that can be sampled to generate new data. AEs are quite a novel branch of models in machine learning. These are sometimes difficult to understand at first. …