The intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application.
Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs.
By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.

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Spis treści

Generative AI Foundations in Python. Discover key techniques and navigate modern challenges in LLMs eBook -- spis treści

• 1. Understanding Generative AI: An Introduction
• 2. Surveying GenAI Types and Modes: An Overview of GANs, Diffusers, and Transformers
• 3. Tracing the Foundations of Natural Language Processing and the Impact of the Transformer
• 4. Applying Pretrained Generative Models: From Prototype to Production
• 5. Fine-Tuning Generative Models for Specific Tasks
• 6. Understanding Domain Adaptation for Large Language Models
• 7. Mastering the Fundamentals of Prompt Engineering
• 8. Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI