Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, Chatgpt, and Other Llms

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.

本書深入探討生成式人工智慧的世界，從神經網路的基礎知識到ChatGPT和Google Bard等大型語言模型的細節。它是一本全方位的資源，適合任何對於理解和應用這項轉型技術感興趣的人，特別是那些剛開始接觸生成式人工智慧的人。

《初學者的應用生成式人工智慧》以詳細的章節結構引導讀者從基礎知識到實際應用。它從生成式人工智慧的介紹和現況開始，探索神經網路的演進如何導致大型語言模型的發展。接著，本書深入探討了ChatGPT和Google Bard等特定架構，並提供使用Sklearn等工具進行實作的實際示範。你還將獲得在企業環境中實施生成式人工智慧的策略性洞察，作者們涵蓋了關鍵主題，如LLMOps、技術堆疊選擇和情境學習。本書的後半部分探討了圖像生成式人工智慧，並提供了行業特定的應用案例，使其成為各個領域實際應用的全面指南。

無論你是一位希望實施先進模型的資料科學家，一位希望利用人工智慧促進企業增長的商業領導者，還是一位對尖端進展感興趣的學術界人士，本書提供了一個簡潔而全面的指南，平衡了理論知識和實踐見解。

你將學到什麼：
- 從神經網路的基礎知識開始，深入了解生成式人工智慧，並進一步瞭解ChatGPT和Google Bard等複雜架構
- 使用Sklearn實作大型語言模型，包括代碼示例和實際應用的最佳實踐
- 學習如何在企業中整合語言模型，包括LLMOps和技術堆疊選擇等方面
- 通過詳細的應用案例和可行的見解，瞭解生成式人工智慧如何應用於各個行業，從醫療保健和市場營銷到法律合規

本書適合對生成式人工智慧和大型語言模型感興趣的資料科學家、人工智慧從業者、研究人員和軟體工程師。

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 such as 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.

Akshay Kulkarni 是一位人工智慧和機器學習的倡導者和IT領導者。他協助過許多財富500強和全球企業，利用人工智慧和數據科學推進戰略轉型。他是Google開發者專家、作家，並經常在主要的人工智慧和數據科學會議上演講（包括Strata、O'Reilly AI Conf和GIDS）。他還是印度一些頂尖研究所的客座教師。2019年，他被評為印度40位40歲以下的頂尖數據科學家之一。他喜歡閱讀、寫作、編程和建立下一代人工智慧產品。

Adarsha S 是一位數據科學和ML Ops（機器學習運營）領導者。目前，他致力於建立世界一流的ML Ops能力，以確保使用人工智慧持續創造價值。他的目標是在組織內外建立一支優秀的數據科學家團隊，通過培訓計劃解決問題，並始終保持領先。他曾在製藥、醫療保健、消費品、零售和營銷行業工作。他居住在班加羅爾，喜歡閱讀和教授數據科學。

Anoosh Kulkarni 是一位數據科學家和ML Ops工程師。他曾與多個全球企業合作，跨多個領域使用機器學習和人工智慧解決他們的業務問題。他曾在阿聯酋領先的電子商務巨頭Awok-dot-com工作，專注於建立最先進的推薦系統和基於深度學習的搜索引擎。他熱衷於指導和指導人們在數據科學之旅中。他經常主持數據科學/機器學習聚會，幫助有抱負的數據科學家制定他們的職業規劃。

Dilip Gudivada 是一位經驗豐富的高級數據架構師，擁有13年的雲服務、大數據和數據工程經驗。Dilip在設計和開發ETL解決方案方面具有豐富的背景，專注於在Azure雲平台上建立強大的數據湖。通過利用Azure Databricks、Data Factory、Data Lake Storage、PySpark、Synapse和Log Analytics等技術，Dilip幫助組織在Azure上建立可擴展和高效的數據湖解決方案。他對雲服務有深入的了解，並且在成功交付數據工程項目方面有著良好的記錄。