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Microsoft Azure AI Fundamentals AI-900 Exam Guide

You're reading from   Microsoft Azure AI Fundamentals AI-900 Exam Guide Gain proficiency in Azure AI and machine learning concepts and services to excel in the AI-900 exam

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835885666
Length 288 pages
Edition 1st Edition
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Authors (2):
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Steve Miles Steve Miles
Author Profile Icon Steve Miles
Steve Miles
Aaron Guilmette Aaron Guilmette
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Aaron Guilmette
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Identify Features of Common AI Workloads FREE CHAPTER
2. Chapter 1: Identify Features of Common AI Workloads 3. Chapter 2: Identify the Guiding Principles for Responsible AI 4. Part 2: Describe the Fundamental Principles of Machine Learning on Azure
5. Chapter 3: Identify Common Machine Learning Techniques 6. Chapter 4: Describe Core Machine Learning Concepts 7. Chapter 5: Describe Azure Machine Learning Capabilities 8. Part 3: Describe Features of Computer Vision Workloads on Azure
9. Chapter 6: Identify Common Types of Computer Vision Solutions 10. Chapter 7: Identify Azure Tools and Services for Computer Vision Tasks 11. Part 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure
12. Chapter 8: Identify Features of Common NLP Workload Scenarios 13. Chapter 9: Identify Azure Tools and Services for NLP Workloads 14. Part 5: Describe Features of Generative AI Workloads on Azure
15. Chapter 10: Identify Features of Generative AI Solutions 16. Chapter 11: Identify Capabilities of Azure OpenAI Service 17. Chapter 12: Accessing the Online Practice Resources 18. Index 19. Other Books You May Enjoy

Describe code generation capabilities of Azure OpenAI Service

GPT models possess the capability to interpret NL or code excerpts and convert them into executable code. OpenAI’s GPT models exhibit proficiency across a wide array of languages, including C#, JavaScript, Perl, PHP, PowerShell, Ruby, Swift, TypeScript, SQL, and Go—though Python is its strongest suit.

These models undergo training on both NL and vast repositories containing billions of lines of code. They excel in generating code based on NL instructions, including code comments, and can provide suggestions for completing code functions.

Codex, a descendent of GPT-3, has been trained on a variety of code samples and repositories in different languages and can answer code completion or review tasks.

In this example, a GPT has been asked to generate a code example that adds up all of the numbers between 1 and 100:

Figure 11.18 – Instructing GPT to generate a code example

Figure 11.18 – Instructing GPT to generate a code example...

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