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Mastering Machine Learning on AWS

You're reading from   Mastering Machine Learning on AWS Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789349795
Length 306 pages
Edition 1st Edition
Languages
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Authors (2):
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Maximo Gurmendez Maximo Gurmendez
Author Profile Icon Maximo Gurmendez
Maximo Gurmendez
Dr. Saket S.R. Mengle Dr. Saket S.R. Mengle
Author Profile Icon Dr. Saket S.R. Mengle
Dr. Saket S.R. Mengle
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Toc

Table of Contents (24) Chapters Close

Preface 1. Section 1: Machine Learning on AWS FREE CHAPTER
2. Getting Started with Machine Learning for AWS 3. Section 2: Implementing Machine Learning Algorithms at Scale on AWS
4. Classifying Twitter Feeds with Naive Bayes 5. Predicting House Value with Regression Algorithms 6. Predicting User Behavior with Tree-Based Methods 7. Customer Segmentation Using Clustering Algorithms 8. Analyzing Visitor Patterns to Make Recommendations 9. Section 3: Deep Learning
10. Implementing Deep Learning Algorithms 11. Implementing Deep Learning with TensorFlow on AWS 12. Image Classification and Detection with SageMaker 13. Section 4: Integrating Ready-Made AWS Machine Learning Services
14. Working with AWS Comprehend 15. Using AWS Rekognition 16. Building Conversational Interfaces Using AWS Lex 17. Section 5: Optimizing and Deploying Models through AWS
18. Creating Clusters on AWS 19. Optimizing Models in Spark and SageMaker 20. Tuning Clusters for Machine Learning 21. Deploying Models Built in AWS 22. Other Books You May Enjoy Appendix: Getting Started with AWS

Implementing facial analysis

Amazon Rekognition also offers a powerful tool for performing facial analysis on images. It can predict interesting attributes, such as age and gender, based on looking at the image. It can also detect features such as a smile or whether the person is wearing glasses from this model. Such models can be trained by analyzing a lot of labeled facial images and training an image recognition model to recognize these features. The CNN models that we studied in Chapter 7, Implementing Deep Learning Algorithms, are a good fit for such applications as they can automatically generate feature maps using local receptive fields methodology from the image and detect boxes that contain evidence of these facial features.

The facial analysis demo can be accessed in the same way as the object detection demo. In order to test the model, we picked the painting of Mona...

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