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\nPearson Professional Computing_April 2024 Titles  \nMicrosoft Press 9780138280376 Pub Date: 3/31/2024  \nOn Sale Date: 4/10/2024  \nShip Date: 3/31/2024 $44.99  \nDiscount Code: y Paperback  \n250 Pages Carton Qty: 0  \nComputers / Software Development & Engineering COM051230  \nSeries: Developer Reference  \nStatus: FORTHCOMING  \nProgramming Microsoft Azure Open AI  \nPrompt Engineering with Large Language Models (LLM) and Cognitive Services  \nFrancesco Esposito  \nKey Selling Points:  \n\n| Learn to use the power of Microsoft's Azure OpenAI framework to program using Large Language Models (LLMs) and Generative AI\u003Cbr> Illustrates and demonstrates new classes of applications affected by AI\u003Cbr> Shows how easy the technical part can be and how the human vision and creativity can take the reins of AI\u003Cbr> Azure OpenAI can be used on any platform, not just .NET\u003Cbr>Summary |\n| --- |\n| The advent of LLMs represents a breakthrough that makes possible things which weren't possible before. For several types of applications, in fact, the user interface, as we know it today, is destined to change forever, replaced by a conversational,\u003Cbr>task-based approach in which users just talk or write to the application and the application understands and responds. It's not magic, but the simple outcome of applying a new concept-prompt engineering. Prompt engineering is crucial to instruct an LLM (e.g., GPT4) to face different tasks without re-training. LLMs today have the power to reason about any unknown data, without dedicated training, as long as they are given appropriate prompts. This is a game-changer for the software industry in much the same way as was the advent of commercial Internet over two decades ago.\u003Cbr>This book is your guide to harnessing this new power. It is split in three parts. The first discusses the set of tools available in the ecosystem of Azure Open AI services. . .\u003Cbr>Contributor Bio |\n\nFrancesco Esposito serves as the Head of Development in a sport-tech company and leads teams behind the platform that run every-day, worldwide operations for professional tennis and padel tennis. He leads the computer-vision team and practices every day with creative ways to increase internal productivity. Franseco co-founded a data science company that is experimenting with digital therapy and is. . .  \nTable of Contents  \nChapter 1-Conversational Programming Chapter 2-Service Models Topology Chapter 3-Cognitive Services Overview Chapter 4-Core Prompt Learning Techniques  \nChapter 5-Engineering Advanced Learning Prompts Chapter 6-Security Concerns in Prompt Engineering Chapter 7-Chat with Your Data  \nChapter 8-Structured Data, Raw Input and Business Logic Chapter 9-Even Simpler Business Apps  \nFBF 23-A.I.  \nNO JACKET  \nAddison-Wesley Professional 9780138179601 Pub Date: 7/26/2024  \nOn Sale Date: 8/5/2024  \nShip Date: 7/16/2024  \n$59.99 Discount Code: y Paperback  \n600 Pages Carton Qty: 0  \nComputers / Languages COM051360  \nSeries: Addison-Wesley Data & Analytics Series  \nStatus: FORTHCOMING  \nApplied Machine and Deep Learning with TensorFlow and PyTorch  \nJustin London  \nKey Selling Points:  \n\n| Master real-world machine learning applications through new case studies using state-of-the-art methods\u003Cbr> Covers leading solutions for natural language processing, computer vision, object detection, image processing, pattern recognition, prediction, data visualization, and more\u003Cbr> Provides TensorFlow and PyTorch code for all case study applications\u003Cbr> Includes valuable tips and insights for improving prediction results\u003Cbr> Covers crucial deployment topics most books neglect\u003Cbr> Promotes understanding with many figures, images, and visual aids\u003Cbr>Summary |\n| --- |\n| This is the definitive practitioner's guide to solving today's real-world problems with machine learning, using tools that are soaring in popularity and marketplace demand. Pioneerin","cbCaimHTzD13Umcb","https://ap.wps.com/l/cbCaimHTzD13Umcb","pdf",1451819,74,"English","# Prompt Engineering with Large Language Models (LLM) and Cognitive Services\n## Summary\n# Applied Machine and Deep Learning with TensorFlow and PyTorch\n## Summary\n# Generative Analysis\n## The Power of Generative AI for Object-Oriented Software Engineering with UML","[{\"question\":\"What does the catalogue emphasize about prompt engineering with Azure OpenAI?\",\"answer\":\"It focuses on how to apply prompt engineering to instruct LLMs for different tasks without re-training, and on building conversational, task-based applications.\"},{\"question\":\"Which toolkits and methods are highlighted for applied machine learning in the TensorFlow and PyTorch title?\",\"answer\":\"The catalogue notes state-of-the-art methods and provides TensorFlow and PyTorch code across domains such as NLP, computer vision, object detection, prediction, and data visualization.\"},{\"question\":\"How does the catalogue connect generative AI to object-oriented software engineering?\",\"answer\":\"It introduces “Generative Analysis,” describing a new method of object-oriented analysis supported by generative AI and illustrated through UML.\"}]","Pearson Professional Computing - A.I. Autumn 2023 Catalogue | PDF",1789891637,186]