The document is a presentation on computer vision for developers using AWS Rekognition. It includes sections on background information on AI, machine learning and deep learning. It discusses supervised vs unsupervised learning and reinforcement learning. It provides examples of computer vision applications such as recycling, OCR, banking, medical and more. It demonstrates Rekognition by showing an example workflow of a photo store app that detects objects and text in images and indexes them in Elasticsearch. It concludes with a demo of Rekognition's object, scene, face and text detection capabilities.

1. MIDWEST | CHICAGO

2. Computer Vision for
Developers
AWS Rekognition | 2020

3. Software Engineer @ SPR
Mena Salwans
menasalwans
mena.salwans@spr.com
mSalwans

4. Agenda:
Computer
Science/IT
Math and
Statistics
Domains/Business
Knowledge
Data
Science
Machine
learning
Software
Development
Traditional
Research
• Background
• Computer vision
• Examples
• Demo

5. AI emphasizes the creation of intelligent
machines that work and react like humans.
Machine learning algorithms build a
mathematical model based on sample data,
known as "training data", in order to make
predictions or decisions effectively without
using explicit instructions, relying on patterns.
AI vs. ML vs. DL
Deep
Learning
Machine
Learning
Artificial
Intelligence

6. Deep learning is part of a broader family of
machine learning methods based on
artificial neural networks with
representation learning. The adjective
"deep" in deep learning comes from the use
of multiple layers in the network.
Computer vision and speech recognition
are two of the most successful applications
of Deep Learning ARTIFICIAL NEURAL NETWORK
Input
Hidden
Output
Deep Learning

7. Supervised vs. Unsupervised Learning
Unsupervised Learning
Takes data that contains only
inputs, and find structure in
the data, like grouping or
clustering of data points.
Supervised Learning
Each training example has
one or more inputs and a
desired output

8. Reinforcement Learning
It’s an area of machine learning concerned
with how software agents ought to take
actions in an environment to maximize some
notion of cumulative reward
https://aws.amazon.com/deepracer/

9. Recycling
OCR
Banking
Medical
Automotive
Retail
Computer Vision Applications

10. Note: This works when copying entire slides from other presentations as long as the source presentation is also 16:9

11. Note: This works when copying entire slides from other presentations as long as the source presentation is also 16:9

12. Photo Store
Amazon S3
Users captures with a
camera, an image
that contains object,
scenes or text. The
mobile/web app
uploads the new
image to S3.
Lamdba Function
A Lambda Function
is triggered and
calls Rekognition.
Rekognition
Rekognition retrieves
the image from S3
and returns labels for
the detected image.
Elasticsearch
Lambda also
pushes the labels
and confidence
scores into
Elasticsearch.
Output
Other users can
search for and
view the image
that contains text.
λ

13. Demo
1. Object and scene detection
2. Face recognition
3. Celebrity recognition
4. Text recognition
github.com/mSalwans/aws-rekognition-dot-net-example

14. github.com/mSalwans/aws-rekognition-dot-net-example
Menasalwans
mena.salwans@spr.co
m
Thank you!

15. github.com/mSalwans/aws-rekognition-dot-net-example
Menasalwans
mena.salwans@spr.co
m
Q & A