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230704 ||| eng |
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|a 9781805122838
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| 050 |
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4 |
|a QA76.9.N38
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| 100 |
1 |
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|a Alto, Valentina
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| 245 |
0 |
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|a Modern generative AI with ChatGPT and OpenAI models
|b leverage the capabilities of OpenAI's LLM for productivity and innovation with GPT3 and GPT4
|c Valentina Alto
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| 250 |
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|a [First edition]
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| 260 |
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|a Birmingham, UK
|b Packt Publishing Ltd.
|c 2023
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| 300 |
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|a 286 pages
|b illustrations
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| 505 |
0 |
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|a Cover -- Title Page -- Copyright and credits -- Contributors -- Table of Contents -- Preface -- Part 1: Fundamentals of Generative AI and GPT Models -- Chapter 1: Introduction to Generative AI -- Introducing generative AI -- Domains of generative AI -- Text generation -- Image generation -- Music generation -- Video generation -- The history and current status of research -- Summary -- References -- Chapter 2: OpenAI and ChatGPT -- Beyond the Market Hype -- Technical requirements -- What is OpenAI? -- An overview of OpenAI model families -- Road to ChatGPT: the math of the model behind it -- The structure of RNNs -- The main limitations of RNNs -- Overcoming limitations -- introducing transformers -- GPT-3 -- ChatGPT: the state of the art -- Summary -- References -- Part 2: ChatGPT in Action -- Chapter 3: Getting Familiar with ChatGPT -- Setting up a ChatGPT account -- Familiarizing yourself with the UI -- Organizing chats -- Summary -- References -- Chapter 4: Understanding Prompt Design -- What is a prompt and why is it important? -- Zero-, one-, and few-shot learning -- typical of transformers models -- Principles of well-defined prompts to obtain relevant and consistent results -- Avoiding the risk of hidden bias and taking into account ethical considerations in ChatGPT -- Summary -- References -- Chapter 5: Boosting Day-to-Day Productivity with ChatGPT -- Technical requirements -- ChatGPT as a daily assistant -- Generating text -- Improving writing skills and translation -- Quick information retrieval and competitive intelligence -- Summary -- Chapter 6: Developing the Future with ChatGPT -- Why ChatGPT for developers? -- Generating, optimizing, and debugging code -- Generating documentation and code explainability -- Understanding ML model interpretability -- Translation among different programming languages -- Summary
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|a Includes bibliographical references and index
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| 505 |
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|a Microsoft Bing and the Copilot system -- The impact of generative technologies on industries -- a disruptive force -- Unveiling concerns about Generative AI -- Elon Musk calls for stopping development -- ChatGPT was banned in Italy by the Italian "Garante della Privacy" -- Ethical implications of Generative AI and why we need Responsible AI -- What to expect in the near future -- Summary -- References -- Index -- Other Books You May Enjoy
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| 505 |
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|a Chapter 7: Mastering Marketing with ChatGPT -- Technical requirements -- Marketers' need for ChatGPT -- New product development and the go-to-market strategy -- A/B testing for marketing comparison -- Boosting Search Engine Optimization (SEO) -- Sentiment analysis to improve quality and increase customer satisfaction -- Summary -- Chapter 8: Research Reinvented with ChatGPT -- Researchers' need for ChatGPT -- Brainstorming literature for your study -- Providing support for the design and framework of your experiment -- Generating and formatting a bibliography -- Generating a presentation of the study -- Summary -- References -- Part 3: OpenAI for Enterprises -- Chapter 9: OpenAI and ChatGPT for Enterprises -- Introducing Azure OpenAI -- Technical requirements -- OpenAI and Microsoft for enterprise-level AI -- introducing Azure OpenAI -- Microsoft AI background -- Azure OpenAI Service -- Exploring Playground -- Why introduce a public cloud? -- Understanding responsible AI -- Microsoft's journey toward responsible AI -- Azure OpenAI and responsible AI -- Summary -- References -- Chapter 10: Trending Use Cases for Enterprises -- Technical requirements -- How Azure OpenAI is being used in enterprises -- Contract analyzer and generator -- Identifying key clauses -- Analyzing language -- Flagging potential issues -- Providing contract templates -- Frontend with Streamlit -- Understanding call center analytics -- Parameter extraction -- Sentiment analysis -- Classification of customers' requests -- Implementing the frontend with Streamlit -- Exploring semantic search -- Document embedding using LangChain modules -- Creating a frontend for Streamlit -- Summary -- References -- Chapter 11: Epilogue and Final Thoughts -- Recap of what we have learned so far -- This is just the beginning -- The advent of multimodal large language models
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| 653 |
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|a Intelligence artificielle / Logiciels
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| 653 |
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|a Artificial intelligence / Computer programs / http://id.loc.gov/authorities/subjects/sh85008181
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| 653 |
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|a ChatGPT. / http://id.loc.gov/authorities/names/n2023013228
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| 653 |
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|a Traitement automatique des langues naturelles
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| 653 |
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|a Natural language processing (Computer science) / fast
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| 653 |
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|a Artificial intelligence / Computer programs / fast
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| 653 |
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|a Natural language processing (Computer science) / http://id.loc.gov/authorities/subjects/sh88002425
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 1805122835
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| 776 |
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|z 9781805123330
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| 776 |
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|z 9781805122838
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781805123330/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3/5
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| 082 |
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|a 500
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| 520 |
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|a Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You'll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you'll explore use cases where ChatGPT can boost productivity and enhance creativity. You'll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You'll also discover enterprise-level scenarios that leverage OpenAI models' APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you'll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you'll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models' APIs in your own projects
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