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Serverless Machine Learning with Amazon Redshift ML

You're reading from   Serverless Machine Learning with Amazon Redshift ML Create, train, and deploy machine learning models using familiar SQL commands

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781804619285
Length 290 pages
Edition 1st Edition
Languages
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Authors (4):
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Phil Bates Phil Bates
Author Profile Icon Phil Bates
Phil Bates
Sumeet Joshi Sumeet Joshi
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Sumeet Joshi
Debu Panda Debu Panda
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Debu Panda
Bhanu Pittampally Bhanu Pittampally
Author Profile Icon Bhanu Pittampally
Bhanu Pittampally
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
2. Chapter 1: Introduction to Amazon Redshift Serverless FREE CHAPTER 3. Chapter 2: Data Loading and Analytics on Redshift Serverless 4. Chapter 3: Applying Machine Learning in Your Data Warehouse 5. Part 2:Getting Started with Redshift ML
6. Chapter 4: Leveraging Amazon Redshift ML 7. Chapter 5: Building Your First Machine Learning Model 8. Chapter 6: Building Classification Models 9. Chapter 7: Building Regression Models 10. Chapter 8: Building Unsupervised Models with K-Means Clustering 11. Part 3:Deploying Models with Redshift ML
12. Chapter 9: Deep Learning with Redshift ML 13. Chapter 10: Creating a Custom ML Model with XGBoost 14. Chapter 11: Bringing Your Own Models for Database Inference 15. Chapter 12: Time-Series Forecasting in Your Data Warehouse 16. Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models 17. Index 18. Other Books You May Enjoy

Introduction to deep learning

Deep learning is a type of artificial intelligence (AI) that uses algorithms to analyze and learn data to draw output similar to the way humans do. Deep learning can leverage both supervised and unsupervised learning using artificial neural networks (ANNs). In deep learning, a set of outputs is generated from the input layers using a feedforward ANN called an MLP. The MLP utilizes backpropagation to feed the errors from the outputs back into the layers to compute one layer at a time and iterates until the model has learned the patterns and relationships in the input data to arrive at a specific output.

Feature learning is a set of techniques where the machine uses raw data to derive the characteristics of a class in the data to derive a specific task at hand. Deep learning models use feature learning efficiently to learn complex, redundant, and variable input data and classify the specified task. Thus, it eliminates the need for manual feature engineering...

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