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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch - Second Edition

By : Joseph Babcock, Raghav Bali
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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch

5 (1)
By: Joseph Babcock, Raghav Bali

Overview of this book

Become an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

Summary

In this chapter, we looked at how the Stable Diffusion algorithm was developed and how it is implemented through the Hugging Face pipeline API. In the process, we saw how a diffusion model addresses conceptual problems with autoregressive transformer and GAN models by modeling the distribution of natural pixels. We also saw how this generative diffusion process can be represented as a reversible Markov process, and how we can train the parameters of a diffusion model using a variational bound, similar to a VAE.

Furthermore, we saw how the efficiency of a diffusion model is improved by executing the forward and reverse process in latent space in the Stable Diffusion model. We also illustrated how natural language user prompts are represented as byte encodings and transformed into numerical vectors. Finally, we looked at the role of the VAE in generating compressed image vectors, and how the U-Net of Stable Diffusion uses the embedded user prompt and a vector of random numbers...

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