Why generative models?

Now that we have reviewed what generative models are and defined them more formally in the language of probability, why would we have a need for such models in the first place? What value do they provide in practical applications? To answer this question, let us take a brief tour of the topics that we will cover in more detail in the rest of this book.

The promise of deep learning

As noted previously, many of the models we will survey in the book are deep, multi-level neural networks. The last 15 years have seen a renaissance in the development of deep learning models for image classification, natural language processing (NLP) and understanding, and reinforcement learning. These advances were enabled by breakthroughs in traditional challenges in tuning and optimizing very complex models, combined with access to larger datasets, distributed computational power in the cloud, and frameworks such as PyTorch, which make it easier to prototype and reproduce research...