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Learn Amazon SageMaker

You're reading from   Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801817950
Length 554 pages
Edition 2nd Edition
Languages
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Author (1):
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Julien Simon Julien Simon
Author Profile Icon Julien Simon
Julien Simon
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Amazon SageMaker
2. Chapter 1: Introducing Amazon SageMaker FREE CHAPTER 3. Chapter 2: Handling Data Preparation Techniques 4. Section 2: Building and Training Models
5. Chapter 3: AutoML with Amazon SageMaker Autopilot 6. Chapter 4: Training Machine Learning Models 7. Chapter 5: Training CV Models 8. Chapter 6: Training Natural Language Processing Models 9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks 10. Chapter 8: Using Your Algorithms and Code 11. Section 3: Diving Deeper into Training
12. Chapter 9: Scaling Your Training Jobs 13. Chapter 10: Advanced Training Techniques 14. Section 4: Managing Models in Production
15. Chapter 11: Deploying Machine Learning Models 16. Chapter 12: Automating Machine Learning Workflows 17. Chapter 13: Optimizing Prediction Cost and Performance 18. Other Books You May Enjoy

Discovering the CV built-in algorithms in Amazon SageMaker

SageMaker includes three CV algorithms, based on proven deep learning networks. In this section, you'll learn about these algorithms, what kind of problem they can help you solve, and what their training scenarios are:

  • Image classification assigns one or more labels to an image.
  • Object detection detects and classifies objects in an image.
  • Semantic segmentation assigns every pixel of an image to a specific class.

Discovering the image classification algorithm

Starting from an input image, the image classification algorithm predicts a probability for each class present in the training dataset. This algorithm is based on the ResNet convolutional neural network (https://arxiv.org/abs/1512.03385). Published in 2015, ResNet won the ILSVRC classification task that same year (http://www.image-net.org/challenges/LSVRC/). Since then, it has become a popular and versatile choice for image classification...

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