Amit Bahree – författare
708 kr
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Part of Microsoft''s radical WinFX API is the Indigo foundation, more formally known as the Windows Communication Foundation, or WCF. Pro WCF: Practical Microsoft SOA Implementation is a complete guide to WCF from the service-oriented architecture (SOA) perspective and shows you why WCF is important to web service development and architecture.
The book covers the unified programming model, reliable messaging, security, the peer-to-peer programming model, and more. Youll also learn how to move your current DCOM and .NET remoting applications to WCF, and how to integrate those applications with new WCF-based applications. Youll want to get ahold of a copy because it:
Contains a comprehensive WCF programming model Explains how queue management and reliable messaging work in WCF Discusses implementing transaction support in WCF Shows how to make WCF services interoperable with other SOA offerings Thoroughly covers WCF security topics and concerns
605 kr
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Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There''s a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models.
You''ll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build.
Get up to speed on the field of weak supervision, including ways to use it as part of the data science processUse Snorkel AI for weak supervision and data programmingGet code examples for using Snorkel to label text and image datasetsUse a weakly labeled dataset for text and image classificationLearn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling
605 kr
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Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There''s a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models.
You''ll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build.
Get up to speed on the field of weak supervision, including ways to use it as part of the data science processUse Snorkel AI for weak supervision and data programmingGet code examples for using Snorkel to label text and image datasetsUse a weakly labeled dataset for text and image classificationLearn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling
671 kr
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507 kr
Skickas inom 3-6 vardagar
414 kr
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Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action!Generative AI in Action is the comprehensive and concrete guide to generative AI you've been searching for. It introduces both AI's fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution.Inside Generative AI in Action you will find:
A practical overview of of generative AI applications Architectural patterns, integration guidance, and best practices for generative AI The latest techniques like RAG, prompt engineering, and multi-modality The challenges and risks of generative AI like hallucinations and jailbreaks How to integrate generative AI into your business and IT strategyGenerative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You'll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale.
About the technology: In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading!
453 kr
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