Carlos Rodriguez

Generative AI Foundations in Python (eBook, ePUB)

Discover key techniques and navigate modern challenges in LLMs

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 190
• Erscheinungstermin: 26. Juli 2024
• Englisch
• ISBN-13: 9781835464915
• Artikelnr.: 71325443

Autorenporträt

Carlos Rodriguez is the Director of AI risk at a major financial institution, where he oversees the validation of cutting-edge AI and machine learning models, including generative AI, to ensure that they remain trustworthy, unbiased, and compliant with stringent regulatory standards. With a degree in data science, numerous professional certifications, and two decades of experience in emerging technology, Carlos is a recognized expert in natural language processing and machine learning. Throughout his career, he has fostered and led high-performing machine learning engineering and data science teams specializing in natural language processing and AI risk, respectively. Known for his human-centered approach to AI, Carlos is a passionate autodidact who continuously expands his knowledge as a data scientist, machine learning practitioner, and risk executive. His current focus lies in developing a comprehensive framework for evaluating generative AI models within a regulatory setting, aiming to set new industry standards for responsible AI adoption and deployment.