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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

Understanding traditional time series forecasting

Let's begin by looking at traditional time series forecasting techniques, specifically ARIMA and exponential smoothing to model demand in simple use cases. We will look at how ARIMA estimates sales using historical sales and forecast errors. Also, we'll review how exponential smoothing accounts for irregularities in historical sales and captures trends and seasonality to forecast sales.

Auto-Regressive Integrated Moving Average (ARIMA )

ARIMA is a time series analytical technique used to capture different temporal structures in univariate data. To model the time series data, differencing is applied across the series to make the data stationary. Differencing is the...

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