Feb 28th Chicago AWS user group event on Machine Learning & Chicago Hot Dogs AWS DeepLens overview and a quick "hotdog/not hotdog" demo - Margaret Valterria, Program Manager at Morningstar // @margaretvaltie Hot Dog Recognition: The model takes the video stream from your AWS DeepLens device as input, and labels images as a hot dog or not a hot dog. The project uses a pretrained, optimized model that is ready to be deployed to your AWS DeepLens device. After deploying the model, you can use the Live View feature to watch as the model recognizes hot dogs.

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Margaret Valtierra
AWS DeepLens:
A New Way to Learn Machine Learning
Chicago AWS user group
@margaretvaltie

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Now, let’s see it in action….
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* Amazon SageMaker Ground Truth
* Amazon SageMaker Neo
* TensorFlow on AWS
* Textract * Personalize * Forecast
Slightly outdated AWS ML service stack

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1. MACHINE LEARNING OVERVIEW
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5 second history of deep learning
Artificial Intelligence (AI)
Machine Learning (ML)
Deep Learning
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Model training Inference
OVERVIEW OF DEEP LEARNING
Data
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DATA
Annotate Preprocess Data split
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2. DEEPLENS OVERVIEW
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Build -> Deploy -> Predict
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Predicting IRL with DeepLens
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Journey through a DeepLens
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Chicago DeepLens Projects

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Questions?
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DeepLens Library

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Resources

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Watch the demo:
https://youtu.be/2oMGkWc46Lw

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DEEPLENS SPECIFICATIONS
• Intel Atom Processor
• Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dual band Wi-Fi
• 8 GB RAM
• 16 GB Storage (eMMC)
• 32 GB SD card
• 4 MP camera with MJPEG
• H.264 encoding at 1080p resolution
• 2 USB ports
• Micro HDMI
• Audio out
• AWS Greengrass preconfigured
• clDNN Optimized for MXNet
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