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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 (18 chapters)
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16
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Index

A walk through image generation: Why we need diffusion models

Diffusion models are among the latest and most popular methods for image generation, particularly based on user-provided natural language prompts. The conceptual challenge of this class of image generation model is to create a method that is:

  • Scalable to train and execute
  • Able to generate a diversity of images, including with user-guided prompts
  • Able to generate natural-looking images
  • Has stable training behavior that is possible to replicate easily

One approach to this problem is “autoregressive” models, where the image is generated pixel by pixel, using the prior-generated pixels as successive inputs1. The inputs to these models could be both a set of image pixels and natural language instructions from the user that are encoded into an embedding vector. This approach is slow, as it makes each pixel dependent upon prior steps in the model output. As we’ve seen...

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