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| | <youtube>Vw-bX1RRZGQ</youtube> | | <youtube>Vw-bX1RRZGQ</youtube> |
| − | <youtube>cTsCMTxEPHs</youtube>
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| − | == Projects ==
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| − | [http://www.youtube.com/results?search_query=deeplens+project+community+challenge YouTube search...]
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| − | * [http://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html AWS DeeplLens Project Templates]
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| − | * [http://aws.amazon.com/deeplens/community-projects/ Collection of AWS DeepLens Community Projects]
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| − | <youtube>usvcA2Ibajs</youtube>
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| − | <youtube>ScYKqja-jdc</youtube>
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| − | <youtube>fLjYKyRDDu0</youtube>
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| − | <youtube>dTXbIzhq_po</youtube>
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| − | <youtube>BbFAm7lcnUU</youtube>
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| − | <youtube>5DDRvRHZ1Qs</youtube>
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| − | <youtube>wnTvVB1ojPk</youtube>
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| − | <youtube>tu7TmUQoj6s</youtube>
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| − | <youtube>5VAKcQtoELo</youtube>
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| − | <youtube>SZTo7CqOjvU</youtube>
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| − | * [http://github.com/aws-samples/aws-ml-vision-end2end aws-ml-vision-end2end - Jupyter Notebook tutorials walking through deep learning Frameworks (MXNet, Gluon) to Platforms (SageMaker, DeepLens) for common CV use-cases | GitHub]
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| − | * [http://aws.amazon.com/blogs/machine-learning/deploy-gluon-models-to-aws-deeplens-using-a-simple-python-api/ Deploy Gluon models to AWS DeepLens using a simple Python API]
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| − | == I just got my DeepLens! ==
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| − | * [http://aws.amazon.com/deeplens/resources/ DeepLens Developer Resources]
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| − | ** [http://aws.amazon.com/getting-started/tutorials/configure-aws-deeplens/ Configure Your New AWS DeepLens]
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| − | ** [http://aws.amazon.com/getting-started/tutorials/build-deeplens-project-sagemaker/?trk=gs_card Build an AWS DeepLens Project]
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| − | ** [http://aws.amazon.com/getting-started/tutorials/create-deploy-project-deeplens/?trk=gs_card Creating and Deploying an AWS DeepLens Project]
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| − | ** [http://aws.amazon.com/getting-started/tutorials/extend-deeplens-project/?trk=gs_card Extending Your AWS DeepLens Project]
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| − | * [http://blog.kloud.com.au/2018/02/27/aws-deeplens-part-1-getting-the-deeplens-online/ Part 1 - Getting Online | SAMZAKKOUR @ Kloud]
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| − | * [http://www.lynda.com/Amazon-Web-Services-tutorials/Setting-up-your-AWS-DeepLens/706932/737910-4.html Lynda.com Setting up your AWS DeepLens]
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| − | <youtube>j0DkaM4L6n4</youtube>
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| − | <youtube>nINqpklf7Eo</youtube>
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| − |
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| − | == First Project ==
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| − |
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| − | *[[(Deep) Residual Network (DRN) - ResNet]]
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| − | [http://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html This project] shows you how a deep learning model can detect and recognize objects in a room. The project uses the Single Shot MultiBox Detector (SSD) framework (Reference: [[Object Detection; Faster R-CNN, YOLO, SSD]]) to detect objects with a pretrained [[ResNet-50]] network. The network has been trained on the [http://host.robots.ox.ac.uk/pascal/VOC Pascal Visual Object Classes Challenge (VOC)] dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you. The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.
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| − | Your web browser is the interface between you and your AWS DeepLens device. You perform all of the following activities on the AWS DeepLens console using your browser, open the AWS DeepLens console at [http://console.aws.amazon.com/deeplens/ http://console.aws.amazon.com/deeplens]. For a walkthrough...[http://github.com/awsdocs/aws-deeplens-user-guide/blob/master/doc_source/deeplens-create-deploy-sample-project.md Creating and Deploying an AWS DeepLens Sample Project | GitHub]
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| − | <youtube>xzFwySJYRoE</youtube>
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| − | Extending the Object Detection project: You will capture the events from your AWS DeepLens model and put them in a queue ready for further processing; then extend the models utilizing AWS [[Lambda]] functions.
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| − | <youtube>PbQO3-jYkGo</youtube>
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| − | == SSH ==
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| − |
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| − | * [http://www.deeplearning.team Deep Learning Team | Jovon Weathers]
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| − | <youtube>2eKjcLsBH6E</youtube>
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| − | <youtube>HozP1t3usPM</youtube>
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| | == Processor == | | == Processor == |
