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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://www.slideshare.net/AmazonWebServices/aws-deeplens-workshop-building-computer-vision-applications AWS DeepLens Workshop: Building Computer Vision Applications] | + | * [https://aws.amazon.com/deeplens/ Product page...] |
| − | * [http://blog.soracom.io/aws-deeplens-meets-soracom-fc121858cd70 AWS DeepLens meets SORACOM; cellular enabled cloud-connectivity-as-a-service platform] | + | * [[Getting Started & Project: Object Detection]] |
| | + | * [[More DeepLens Projects]] |
| | + | * [[Video/Image]] |
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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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| | <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>cTsCMTxEPHs</youtube> | + | <youtube>qRJwvo94-Ko</youtube> |
| | | | |
| − | == 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]
| + | https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2017/11/30/DeepLens-Flow-1.gif |
| − | * [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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| − | | |
| − | == First Project ==
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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 Pascal 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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| − | <youtube>xzFwySJYRoE</youtube>
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| − | <youtube>PbQO3-jYkGo</youtube>
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| − | | |
| − | == SSH ==
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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 == | + | == Hardware Specifications == |
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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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