Description

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This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI.

Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You'll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains.

Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights.

What You Will Learn

• Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google Bard
• Implement large language models using Sklearn, complete with code examples and best practices for real-world application
• Learn how to integrate LLM's in enterprises, including aspects like LLMOps and technology stack selection
• Understand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insights

Who This Book Is For

Data scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.

About the Author

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Akshay Kulkarni is an AI and machine learning evangelist and IT leader. He has assisted numerous Fortune 500 and global firms in advancing strategic transformations using AI and data science. He is a Google Developer Expert, author, and regular speaker at major AI and data science conferences (including Strata, O'Reilly AI Conf, and GIDS). He is also a visiting faculty member for some of the top graduate institutes in India. In 2019, he was featured as one of the top 40 under-40 Data Scientists in India. He enjoys reading, writing, coding, and building next-gen AI products. Adarsha S is a data science and ML Ops leader. Presently, he is focused on creating world-class ML Ops capabilities to ensure continuous value delivery using AI. He aims to build a pool of exceptional data scientists within and outside the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked in the pharma, healthcare, CPG, retail, and marketing industries. He lives in Bangalore and loves to read and teach data science. Anoosh Kulkarni is a data scientist and ML Ops engineer. He has worked with various global enterprises across multiple domains solving their business problems using machine learning and AI. He has worked at Awok-dot-com, one of the leading e-commerce giants in UAE, where he focused on building state of art recommender systems and deep learning-based search engines. He is passionate about guiding and mentoring people in their data science journey. He often leads data sciences/machine learning meetups, helping aspiring data scientists carve their career road map. Dilip Gudivada is a seasoned senior data architect with 13 years of experience in cloud services, big data, and data engineering. Dilip has a strong background in designing and developing ETL solutions, focusing specifically on building robust data lakes on the Azure cloud platform. Leveraging technologies suchas Azure Databricks, Data Factory, Data Lake Storage, PySpark, Synapse, and Log Analytics, Dilip has helped organizations establish scalable and efficient data lake solutions on Azure. He has a deep understanding of cloud services and a track record of delivering successful data engineering projects.