The post of today is going to be a bit different. We have already talked about Variational Autoencoders (VAE) in the past, but today we are going to see how to implement it from scratch, train it on a dataset and see how it behaves with tabular data. Yes, VAEs can be used for tabular data as well. To do so, we will use the CRISP-DM framework to guide us through the process. ...
Generative models are a class of machine learning that learn a representation of the data trained on and they model the data itself. Ideally, generative models should satisfy the following key requirements in a real environment: High quality samples refers to those samples that captures the underlying patterns and structures present in the data making them indistinguishable from human observers. Fast Sampling is about the efficiency of image generation and the computational overhead that can cause generative models. Mode Coverage/Diversity points out how the model is able to generate a full range of mods and diverse patterns present in the training data Fig. 1. The Generative Learning Trilemma ...