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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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Index

Prompt engineering

Generative models are powerful systems capable of producing images, text, audio, video, or combinations of modalities, depending on their design and training. In Chapters 5 and 6, we explored transformer-based models that generate text in various languages and styles by providing specific inputs, sometimes with instructions or examples. Throughout this book, we’ve generated outputs conditioned on specific inputs—effectively engaging in prompt engineering all along.

Figure 7.1: Tweet by Andrew Karpathy on Prompt Engineering1

Figure 7.1: Tweet by Andrew Karpathy on Prompt Engineering1

Simply put, prompt engineering is the practice of designing and refining prompts to guide generative models, particularly LLMs, to produce desired outputs. A prompt is the input to these models, often in plain language, consisting of task instructions (implicit or explicit) with or without examples, enabling users to tap into the model’s vast capabilities (see Figure 7.1).

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