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Practical Guide to Azure Cognitive Services

Practical Guide to Azure Cognitive Services

By : Chris Seferlis , Christopher Nellis, Andy Roberts
4.9 (12)
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Practical Guide to Azure Cognitive Services

Practical Guide to Azure Cognitive Services

4.9 (12)
By: Chris Seferlis , Christopher Nellis, Andy Roberts

Overview of this book

Azure Cognitive Services and OpenAI are a set of pre-built artificial intelligence (AI) solution APIs that can be leveraged from existing applications, allowing customers to take advantage of Microsoft’s award-winning Vision, Speech, Text, Decision, and GPT-4 AI capabilities. With Practical Guide to Azure Cognitive Services, you’ll work through industry-specific examples of implementations to get a head-start in your production journey. You’ll begin with an overview of the categorization of Azure Cognitive Services and the benefits of embracing AI solutions for practical business applications. After that, you’ll explore the benefits of using Azure Cognitive Services to optimize efficiency and improve predictive capabilities. Then, you’ll learn how to leverage Vision capabilities for quality control, Form Recognizer to streamline supply chain nuances, language understanding to improve customer service, and Cognitive Search for next-generation knowledge-mining solutions. By the end of this book, you’ll be able to implement various Cognitive Services solutions that will help you enhance efficiency, reduce costs, and improve the customer experience at your organization. You’ll also be well equipped to automate mundane tasks by reaping the full potential of OpenAI.
Table of Contents (22 chapters)
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1
Part 1: Ocean Smart – an AI Success Story
5
Part 2: Deploying Next-Generation Knowledge Mining Solutions with Azure Cognitive Search
10
Part 3: Other Cognitive Services That Will Help Your Company Optimize Operations

Using Reinforcement Learning for recommendations

The core machine learning concept that is the basis for the Personalizer service is Reinforcement Learning. Along with Supervised Learning and Unsupervised Learning, it makes up the basic foundational pillars of machine learning. As discussed previously in the chapter, RL is a mix of the exploration of options and exploitation of an existing model. This concept differs from Supervised Learning because, with this pillar, we are expected to provide feedback for each activity, such as a labeled input/output pair for presentation or feedback when an incorrect option is presented and there’s a need for feedback for correction. With Unsupervised Learning, patterns are learned from untagged data, which will mimic what is extrapolated from the data to build expected predictions.

So, as this method is applied to the service, we take our reward feedback from the application we’re using to better train the model for the exploitation...

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