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| − | [http://www.youtube.com/results?search_query=amazon+deeplens YouTube search...] | + | {{#seo: |
| | + | |title=PRIMO.ai |
| | + | |titlemode=append |
| | + | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS |
| | + | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| | + | }} |
| | + | [https://www.youtube.com/results?search_query=amazon+deeplens YouTube search...] |
| | + | [https://www.google.com/search?q=amazon+deeplens+deep+machine+learning+ML ...Google search] |
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| − | * [http://aws.amazon.com/deeplens/ Product page...] | + | * [[Amazon | Amazon AWS]] |
| − | ** [http://aws.amazon.com/getting-started/tutorials/configure-aws-deeplens/ Configure Your New AWS DeepLens] | + | * [https://aws.amazon.com/deeplens/ Product page...] |
| − | ** [http://aws.amazon.com/deeplens/resources/ DeepLens Developer Resources]
| + | * [[Getting Started & Project: Object Detection]] |
| − | ** [http://aws.amazon.com/getting-started/tutorials/build-deeplens-project-sagemaker/?trk=gs_card Build an AWS DeepLens Project]
| + | * [[More DeepLens Projects]] |
| − | ** [http://aws.amazon.com/getting-started/tutorials/create-deploy-project-deeplens/?trk=gs_card Creating and Deploying an AWS DeepLens Project]
| + | * [[Video/Image]] |
| − | ** [http://aws.amazon.com/getting-started/tutorials/extend-deeplens-project/?trk=gs_card Extending Your AWS DeepLens Project]
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| − | ** [http://aws.amazon.com/deeplens/community-projects/ Community Projets]
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| − | * [http://www.slideshare.net/AmazonWebServices/aws-deeplens-workshop-building-computer-vision-applications AWS DeepLens Workshop: Building Computer Vision Applications]
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| | https://docs.aws.amazon.com/deeplens/latest/dg/images/deeplens-hiw-general.png | | https://docs.aws.amazon.com/deeplens/latest/dg/images/deeplens-hiw-general.png |
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| − | == Integrated Components/Technologies ==
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| − |
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| − | * [[Lex]] - conversational interfaces using voice and text
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| − | * [[SageMaker]] - build, train, and deploy
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| − | * [[Polly]] - text to speech
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| − | * [[Rekognition]] - video analysis service
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| − | * [[Kinesis]] - collect, process, and analyze real-time, streaming data
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| − | * [[Lambda]] - run code without managing servers
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| − | * [https://aws.amazon.com/iot/ AWS IoT] Services Overview
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| − | ** [[Internet of Things (IoT) Core]] - process and route those messages to AWS endpoints
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| − | **[[AWS IoT Button]]
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| − | * [[Greengrass]] - connected devices can run AWS Lambda functions, keep device data in sync
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| − | * [[Intel® Compute Library for Deep Neural Networks (clDNN)]] & OpenVINO - deep learning primitives for computer vision
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| − | * [[Simple Queue Service (SQS)]] - message queuing
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| − | * [[Simple Notification Service (SNS)]] - pub/sub messaging and mobile notifications
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| − | * [[DynamoDB]] - NoSQL database
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| − | * [[Simple Storage Service (S3)]] - object storage
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| − | * [[Management Console]] - manage web services
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| − | * [[Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI]]
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| − | * [http://en.wikipedia.org/wiki/SoftAP SoftAP] - software enabled access point
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| − | * [http://www.ubuntu.com/ Ubuntu] - operating system
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| − |
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| − | == Frameworks ==
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| − |
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| − | * [[TensorFlow]]
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| − | * [[Caffe / Caffe2]]
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| − | * [[MXNet]]
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| − | * [[gluon]]
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| − | https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2017/11/30/DeepLens-Flow-1.gif
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| − | == I just got my DeepLens! ==
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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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| − | * [http://blog.soracom.io/aws-deeplens-meets-soracom-fc121858cd70 AWS DeepLens meets SORACOM; cellular enabled cloud-connectivity-as-a-service platform]
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| − |
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| − | <youtube>j0DkaM4L6n4</youtube>
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| − | <youtube>nINqpklf7Eo</youtube>
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| − |
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| − | == Learn More ==
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| | <youtube>RhEVld4GwzU</youtube> | | <youtube>RhEVld4GwzU</youtube> |
| | <youtube>BqHWG2CUDg4</youtube> | | <youtube>BqHWG2CUDg4</youtube> |
| − | <youtube>qRJwvo94-Ko</youtube>
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| − | <youtube>5m7eSTSwpk4</youtube>
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| | <youtube>bTLMU9z_hes</youtube> | | <youtube>bTLMU9z_hes</youtube> |
| − | <youtube>bSrpk1xQHB4</youtube>
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| | <youtube>Vw-bX1RRZGQ</youtube> | | <youtube>Vw-bX1RRZGQ</youtube> |
| − | <youtube>xzFwySJYRoE</youtube> | + | <youtube>qRJwvo94-Ko</youtube> |
| − | <youtube>PbQO3-jYkGo</youtube>
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| − | <youtube>cTsCMTxEPHs</youtube>
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| | | | |
| − | == Projects ==
| + | https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2017/11/30/DeepLens-Flow-1.gif |
| − | [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 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>5VAKcQtoELo</youtube>
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| − | <youtube>SZTo7CqOjvU</youtube>
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| − | <youtube>tu7TmUQoj6s</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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| − | == Processor == | + | == Hardware Specifications == |
| − | [http://www.youtube.com/results?search_query=deeplens+processor+intel YouTube search...]
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| − | With over 100 GFLOPS of compute power on the device, it can process deep learning predictions on HD video for real time.
| + | * 4 megapixel resolution (1080p [[Video/Image|Video]]) |
| | + | * 8GB of RAM |
| | + | * 16GB of internal storage |
| | + | * A 32GB SD card |
| | + | * Wi-Fi connectivity |
| | + | * A micro HDMI port |
| | + | * 1/8" audio port |
| | + | * 2 USB ports |
| | + | * 5v power supply |
| | + | * The CPU is a [https://ark.intel.com/products/96486/Intel-Atom-x5-E3930-Processor-2M-Cache-up-to-1_80-GHz dual-core Atom E3930]. DeepLens comes with an [https://www.notebookcheck.net/Intel-HD-Graphics-500.182723.0.html Intel HD Graphics 500 chip]; so a graphics processing unit ([https://en.wikipedia.org/wiki/Graphics_processing_unit GPU]) is included too. This chip has 12 “execution units” capable of running 7 threads each [single instruction, multiple data ([https://en.wikipedia.org/wiki/SIMD SIMD]) architecture]. 84 “cores” - over 100 GFLOPS of compute power on the device, it can process deep learning predictions on HD [[Video/Image|video]] for real time. [https://software.intel.com/sites/default/files/managed/c5/9a/The-Compute-Architecture-of-Intel-Processor-Graphics-Gen9-v1d0.pdf Processor Technical Specifications] |
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| − | * [http://software.intel.com/sites/default/files/managed/c5/9a/The-Compute-Architecture-of-Intel-Processor-Graphics-Gen9-v1d0.pdf Processor Technical Specifications]
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| | <youtube>suQnh1TvGHw</youtube> | | <youtube>suQnh1TvGHw</youtube> |
| − | <youtube>TS4ShpBHr_g</youtube>
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| − |
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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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| − |
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| − | <youtube>2eKjcLsBH6E</youtube>
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| − | <youtube>HozP1t3usPM</youtube>
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