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Hands-On Artificial Intelligence on Amazon Web Services

You're reading from   Hands-On Artificial Intelligence on Amazon Web Services Decrease the time to market for AI and ML applications with the power of AWS

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789534146
Length 426 pages
Edition 1st Edition
Tools
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Authors (2):
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Subhashini Tripuraneni Subhashini Tripuraneni
Author Profile Icon Subhashini Tripuraneni
Subhashini Tripuraneni
Charles Song Charles Song
Author Profile Icon Charles Song
Charles Song
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction and Anatomy of a Modern AI Application FREE CHAPTER
2. Introduction to Artificial Intelligence on Amazon Web Services 3. Anatomy of a Modern AI Application 4. Section 2: Building Applications with AWS AI Services
5. Detecting and Translating Text with Amazon Rekognition and Translate 6. Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly 7. Extracting Information from Text with Amazon Comprehend 8. Building a Voice Chatbot with Amazon Lex 9. Section 3: Training Machine Learning Models with Amazon SageMaker
10. Working with Amazon SageMaker 11. Creating Machine Learning Inference Pipelines 12. Discovering Topics in Text Collection 13. Classifying Images Using Amazon SageMaker 14. Sales Forecasting with Deep Learning and Auto Regression 15. Section 4: Machine Learning Model Monitoring and Governance
16. Model Accuracy Degradation and Feedback Loops 17. What Is Next? 18. Other Books You May Enjoy

Using Python for AI applications

Python is one of the most popular programming languages. Thanks to its popularity with the data science and ML community, Python is also one of the fastest-growing programming languages. There is a large number of add-on libraries from its developers and the open source community. These libraries enable Python developers to do almost anything, from data analytics to deep neural networks, from simple scripting to web application development.

For AI and ML, Python is the de facto language. There's the popular scikit-learn library that gives developers access to many useful ML algorithms. There are also many libraries for deep neural networks, such as MXNet and TensorFlow.

We will be using Python for every hands-on projects throughout this book:

  • In the first half of this book, we will create intelligent-enabled solutions using AWS AI services...
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