Generative Deep Learning with Python

Chapter 7: Understanding Autoregressive Models

 

In the rapidly evolving field of deep learning and generative models, autoregressive models have become increasingly important due to their ability to generate high-quality, realistic outcomes by predicting the future based on past data. These models have proven to be particularly powerful because they can capture complex dependencies in the data, which is why they are widely used in a range of areas, including time series forecasting, natural language processing, and image generation.

In this chapter, we will explore two famous types of autoregressive models, PixelRNN and PixelCNN, in greater detail. We'll examine their unique architectures, how they work, how they are trained, and the many nuances ...