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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
Other Books You May Enjoy
17
Index

Summary

In this chapter, we introduced prompt engineering, one of the most exciting new fields to emerge of late. We covered a number of key aspects associated with this field by first presenting a historical need to have a more natural interface to work with computers right from the days of COBOL. We covered details on prompt design fundamentals, diving into topics such as system instructions, prompt templates, and LLM parameters. We then covered a number of good practices and strategies to develop effective prompts. We also covered task-specific prompting techniques and closed the discussion by providing a brief introduction to some advanced prompting techniques, such as Chain of Thought and Tree of Thought. We extended this discussion to provide an overview of prompting best practices for vision/image, audio, and multi-modal models. Throughout the chapter, we also worked through hands-on examples to put the theory into practice. Toward the end of the chapter, we covered the topic...

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