1 What is generative AI and why PyTorch?

 

This chapter covers

  • Generative AI vs. nongenerative AI
  • Why PyTorch is ideal for deep learning and generative AI
  • The concept of Generative Adversarial Networks
  • The benefits of the attention mechanism and Transformers
  • Advantages of creating generative AI models from scratch

Generative AI has significantly affected 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, ushering in a new era in technology and inspiring a host of startups to explore the extensive possibilities offered by various generative models.

Consider the advancements made by Midjourney, a pioneering company, which now creates high-resolution, realistic images from brief text inputs. Similarly, Freshworks, a software company, has accelerated application development dramatically, reducing the time required from an average of 10 weeks to mere days, a feat achieved through the capabilities of ChatGPT (see the Forbes article “10 Amazing Real-World Examples of How Companies Are Using ChatGPT in 2023,” by Bernard Barr, 2023, https://mng.bz/Bgx0). To add a case in point, elements of this very introduction have been enhanced by generative AI, demonstrating its ability to refine content to be more engaging.

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 our AI framework

 
 
 
 

1.2 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 attention mechanism

 
 

1.3.2 The Transformer architecture

 
 

1.3.3 Multimodal Transformers and pretrained LLMs

 
 
 

1.4 Why build generative models from scratch?

 

Summary

 
 
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