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

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800208919

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Julien Simon

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Introduction to Amazon SageMaker

2. Chapter 1: Introduction to 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 Computer Vision 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 on 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

Leave a review - let other readers know what you think

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 problems 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...