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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

Off-the-shelf implementations

In this chapter, we covered a step-by-step approach to developing a deepfake pipeline for re-enactment mode. Though the implementations are easy to understand and execute, they require quite a bit of understanding and resources to generate high-quality results.

Since the release of u/deepfakes’ content in 2017, a number of open-source implementations have come out to simplify the use of this technology. While dangerous, most of these projects highlight the ethical implications and caution developers and users in general against malicious use of such projects. While it is beyond the scope of this chapter, we list a few well-designed, popular implementations in this section. Readers are encouraged to go through specific projects for more details.

  • FaceSwap: https://github.com/Deepfakes/faceswap

The developers of this project claim this implementation is close to the original implementation by u/deepfakes, with enhancements...

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