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Mena Salwans - Computer Vision for developers

Computer Vision for developers
Mena Salwans

Virtual AWS Community Day | Midwest

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Mena Salwans - Computer Vision for developers

  1. 1. MIDWEST | CHICAGO
  2. 2. Computer Vision for Developers AWS Rekognition | 2020
  3. 3. Software Engineer @ SPR Mena Salwans menasalwans mena.salwans@spr.com mSalwans
  4. 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. 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. 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. 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. 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. 9. Recycling OCR Banking Medical Automotive Retail Computer Vision Applications
  10. 10. Note: This works when copying entire slides from other presentations as long as the source presentation is also 16:9
  11. 11. Note: This works when copying entire slides from other presentations as long as the source presentation is also 16:9
  12. 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. 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. 14. github.com/mSalwans/aws-rekognition-dot-net-example Menasalwans mena.salwans@spr.co m Thank you!
  15. 15. github.com/mSalwans/aws-rekognition-dot-net-example Menasalwans mena.salwans@spr.co m Q & A

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