1 What Is Generative AI and Why PyTorch?

This chapter covers

What is generative AI and how is it different from its non-generative counterparts
Why is PyTorch suitable for deep learning and generative AI
The idea behind Generative Adversarial Networks (GANs)
The advantages of the attention mechanism and Transformers
The benefits of building generative AI models from scratch

Generative AI has significantly impacted the global landscape, capturing widespread attention and becoming a focal point since the advent of ChatGPT in November 2022. This technological advancement has revolutionized numerous aspects of everyday life, marking a new era in technology and inspiring a host of startups to explore the extensive possibilities offered by generative AI.

Consider the advancements made by Midjourney, a pioneering company, which now creates high-resolution, realistic images from brief text inputs. Similarly, Freshworks, a leading software company, has accelerated application development dramatically, reducing the time required from an average of ten weeks to mere days, a feat achieved through the capabilities of ChatGPT.[1] Notably, elements of this very introduction have been enhanced by AI, demonstrating its capability to refine content to be more engaging.[2]

1.1 Introducing Generative AI and PyTorch

1.1.1 What Is Generative AI?

1.1.2 The Python Programming Language

1.1.3 Using PyTorch as Your AI Framework

1.2 Generative Adversarial Networks (GANs)

1.2.1 A High-Level Overview of GANs

1.2.2 An Illustrating Example: Generating Anime Faces

1.2.3 Why Should You Care About GANs?

1.3 Transformers

1.3.1 The Self-Attention Mechanism

1.3.2 The Transformer Architecture

1.3.3 Multimodal Transformers and Pre-Trained LLMs

1.4 Why Build Generative Models from Scratch?

1.5 Summary