Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, Chatgpt, and Other Llms
暫譯: 初學者的應用生成式人工智慧：擴散模型、ChatGPT 及其他大型語言模型的實用知識
Kulkarni, Akshay, Shivananda, Adarsha, Kulkarni, Anoosh
相關主題
商品描述
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.
商品描述(中文翻譯)
這本書深入探討生成式人工智慧的世界，涵蓋從神經網絡的基本概念到大型語言模型（如 ChatGPT 和 Google Bard）的複雜性。它是任何對理解和應用這項變革性技術感興趣的人的一站式資源，特別針對那些剛開始接觸生成式人工智慧的讀者。
《應用生成式人工智慧入門》以詳細的章節結構為主，將引導您從基礎知識到實際應用。書中首先介紹生成式人工智慧及其當前的發展現狀，接著探討神經網絡的演變如何促成大型語言模型的發展。然後，書中深入探討像 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。您還將了解在企業環境中實施生成式人工智慧的策略性方面，作者涵蓋了 LLMOps、技術堆疊選擇和上下文學習等重要主題。書的後半部分探討生成式人工智慧在圖像方面的應用，並提供行業特定的案例，使其成為各個領域實際應用的綜合指南。
無論您是希望實施先進模型的數據科學家、旨在利用人工智慧促進企業增長的商業領導者，還是對前沿技術感興趣的學術研究者，這本書都提供了一個簡明而全面的指南，幫助您掌握生成式人工智慧，平衡理論知識與實踐見解。
您將學到的內容：
- 從神經網絡的基本概念開始，逐步深入了解生成式人工智慧，並進入像 ChatGPT 和 Google Bard 等複雜架構
- 使用 Sklearn 實施大型語言模型，並提供實際應用的代碼範例和最佳實踐
- 學習如何在企業中整合大型語言模型，包括 LLMOps 和技術堆疊選擇等方面
- 了解生成式人工智慧如何應用於各行各業，從醫療保健和行銷到法律合規，通過詳細的案例和可行的見解
本書適合對象：
對生成式人工智慧和大型語言模型感興趣的數據科學家、人工智慧從業者、研究人員和軟體工程師。
作者簡介
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.
作者簡介(中文翻譯)
阿克沙伊·庫爾卡尼 是一位人工智慧和機器學習的推廣者及IT領導者。他協助眾多《財富》500強及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、作者，並且是主要人工智慧和數據科學會議（包括Strata、O'Reilly AI Conf和GIDS）的常規演講者。他也是印度一些頂尖研究所的客座教授。2019年，他被評選為印度40位40歲以下的頂尖數據科學家之一。他喜歡閱讀、寫作、編程和構建下一代人工智慧產品。
阿達爾沙·S 是一位數據科學和機器學習運營的領導者。目前，他專注於創建世界級的機器學習運營能力，以確保利用人工智慧持續交付價值。他的目標是在組織內外建立一支卓越的數據科學家團隊，通過培訓計劃解決問題，並始終希望走在潮流的前端。他曾在製藥、醫療保健、消費品、零售和市場營銷行業工作。他住在班加羅爾，熱愛閱讀和教授數據科學。
阿努什·庫爾卡尼 是一位數據科學家和機器學習運營工程師。他曾與多個全球企業合作，解決他們的商業問題，利用機器學習和人工智慧。他曾在阿聯酋的領先電子商務巨頭Awok-dot-com工作，專注於構建最先進的推薦系統和基於深度學習的搜索引擎。他熱衷於指導和輔導人們在數據科學的旅程中前進。他經常主辦數據科學/機器學習的聚會，幫助有志於成為數據科學家的專業人士規劃職業路徑。
迪利普·古迪瓦達 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。迪利普在設計和開發ETL解決方案方面有著堅實的背景，特別專注於在Azure雲平台上構建穩健的數據湖。利用Azure Databricks、Data Factory、Data Lake Storage、PySpark、Synapse和Log Analytics等技術，迪利普幫助組織在Azure上建立可擴展且高效的數據湖解決方案。他對雲服務有深刻的理解，並在交付成功的數據工程項目方面有著良好的記錄。
