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Udacity | Intel® Edge AI for IoT Developers | Nanodegree Program [FCO]

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Udacity | Intel® Edge AI for IoT Developers | Nanodegree Program [FCO] (Size: 1.37 GB)
  0. Websites you may like
  0. (1Hack.Us) Premium Tutorials-Guides-Articles _ Community based Forum.url 377 B
  1. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url 286 B
  3. (FTUApps.com) Download Cracked Developers Applications For Free.url 239 B
  How you can help our Group!.txt 208 B
  Part 01-Module 01-Lesson 01_Nanodegree Program Introduction
  01. Welcome To Udacity V2-9QadFJRKrEA.en.vtt 945 B
  01. Welcome To Udacity V2-9QadFJRKrEA.mp4 4.45 MB
  01. Welcome To Udacity V2-9QadFJRKrEA.zh-CN.vtt 959 B
  01. Welcome to Udacity.html 6.18 KB
  02. Welcome To Your Nanodegree Experience V2-gd0Z0S58Y68.en.vtt 2.14 KB
  02. Welcome To Your Nanodegree Experience V2-gd0Z0S58Y68.mp4 8.78 MB
  02. Welcome To Your Nanodegree Experience V2-gd0Z0S58Y68.zh-CN.vtt 2.01 KB
  02. Welcome to the Nanodegree Program Experience.html 6.3 KB
  03. How to Succeed.html 6.2 KB
  03. Welcome How To Succeed FINAL-hVwUijsLKzw.en.vtt 7.77 KB
  03. Welcome How To Succeed FINAL-hVwUijsLKzw.mp4 35.83 MB
  03. Welcome How To Succeed FINAL-hVwUijsLKzw.zh-CN.vtt 7.27 KB
  04. Welcome to Intel® Edge AI for IoT Developers.html 8.52 KB
  05. Prerequisites _ Other Requirements.html 8.2 KB
  06. Notebooks and Workspaces.html 10.43 KB
  07. Graffiti Tutorial.html 6.66 KB
  08. Access the Career Portal.html 8.45 KB
  img.zip 6.79 MB
  index.html 4.82 KB
  media.zip 459.1 KB
  Part 02-Module 01-Lesson 01_Introduction to AI at the Edge
  01. Instructor Intro.html 6.8 KB
  01. IntelScho L0 01 Instructor Intro V2-KogUwtKPv7Q.en.vtt 1.28 KB
  01. IntelScho L0 01 Instructor Intro V2-KogUwtKPv7Q.mp4 6.37 MB
  02. L0 02 What Is AI At The Edge-4dro5ZHQLa4.en.vtt 1.8 KB
  02. L0 02 What Is AI At The Edge-4dro5ZHQLa4.mp4 6.44 MB
  02. What is AI at the Edge.html 10.2 KB
  03. IntelScho L0 03 Why Is AI At The Edge Important V2-TP3LMX_1uwA.en.vtt 1.85 KB
  03. IntelScho L0 03 Why Is AI At The Edge Important V2-TP3LMX_1uwA.mp4 6.41 MB
  03. Why is AI at the Edge Important.html 7.17 KB
  04. Applications of AI at the Edge.html 7.25 KB
  04. IntelScho L0 04 Applications Of AI At The Edge V2-E1StqFO7j9Q.mp4 6.33 MB
  05. Historical Context.html 8.17 KB
  05. L0 05 Historical Context-Gp0CqY4_TGI.mp4 5.59 MB
  06. Course Structure.html 7.39 KB
  06. L0 06 Course Structure-BD3nzcTAczg.mp4 4.32 MB
  07. L0 07 Why Are The Topics Distinct-ZFrb7ZXJ_bA.mp4 3.35 MB
  07. Why Are the Topics Distinct.html 9.15 KB
  08. L0 08 Relevant Tools And Prerequisites-s3wUC-4M4Uk.mp4 3.73 MB
  08. Relevant Tools and Prerequisites.html 10.06 KB
  09. L0 09 What You Will Build-e38WWETUZ8g.mp4 3.71 MB
  09. Project-Demo-uWkMjUiqk0M.mp4 1.28 MB
  09. What You Will Build.html 7.42 KB
  10. L0 10 Recap-tpZHjx2r5gs.mp4 2.94 MB
  10. Recap.html 6.6 KB
  index.html 4.89 KB
  Part 02-Module 01-Lesson 02_Leveraging Pre-Trained Models
  01. IntelScho L1 01 Introduction V2-vFNZu1VpdwE.en.vtt 491 B
  01. IntelScho L1 01 Introduction V2-vFNZu1VpdwE.mp4 2.1 MB
  01. Introduction.html 7.97 KB
  02. IntelScho L1 02 The Intel Distribution Of OpenVINO Toolkit V2--pM9pLCnzJk.en.vtt 2.32 KB
  02. IntelScho L1 02 The Intel Distribution Of OpenVINO Toolkit V2--pM9pLCnzJk.mp4 11.52 MB
  02. The OpenVINO™ Toolkit.html 10.73 KB
  03. IntelScho L1 03 Pre-Trained Models In The Intel Distribution Of OpenVINO Toolkit-1-Vije0cMBQ.en.vtt 1.5 KB
  03. IntelScho L1 03 Pre-Trained Models In The Intel Distribution Of OpenVINO Toolkit-1-Vije0cMBQ.mp4 6.54 MB
  03. Pre-Trained Models in OpenVINO™.html 9.03 KB
  04. IntelScho L1 04 Types Of Computer Vision Models V2-E8yBgSKfCoo.en.vtt 4.4 KB
  04. IntelScho L1 04 Types Of Computer Vision Models V2-E8yBgSKfCoo.mp4 10.24 MB
  04. Types of Computer Vision Models.html 11.65 KB
  05. Case Studies in Computer Vision.html 12.56 KB
  05. IntelScho L1 05 Case Studies In Computer Vision V2-7mUaovlA4aQ.mp4 5.43 MB
  06. Available Pre-Trained Models in OpenVINO™.html 10.32 KB
  06. IntelScho L1 06 Available Pre-Trained Models-SoTH1jr3-HA.mp4 10.3 MB
  07. Exercise Loading Pre-Trained Models.html 8.06 KB
  08. IntelScho L1 07 Exercise Loading Pre-Trained Models Solution-QMfTUdWFsGw.mp4 14.76 MB
  08. Solution Loading Pre-Trained Models.html 11.51 KB
  09. IntelScho L1 08 Optimizations On The Pre-Trained Models-nKvZYnOnWm4.mp4 2.57 MB
  09. Optimizations on the Pre-Trained Models.html 8.33 KB
  10. Choosing the Right Model for Your App.html 10.65 KB
  10. IntelScho L1 09 Choosing The Right Model For Your App-CWC195DzgAI.mp4 8.34 MB
  11. IntelScho L1 10 Pre-Processing Inputs-E9huKos96Uk.mp4 6.56 MB
  11. Pre-processing Inputs.html 8.69 KB
  12. Exercise Pre-processing Inputs.html 8.04 KB
  13. IntelScho L1 11 Exercise Pre-Processing Inputs Solution-erNsB5nXgW4.mp4 17.04 MB
  13. Solution Pre-processing Inputs.html 9.5 KB
  14. Handling Network Outputs.html 11.99 KB
  14. IntelScho L1 12 Handling Network Outputs-pREe4P5yygM.mp4 6.38 MB
  15. IntelScho L1 13 Running Your First Edge App-FANZZXUqGac.mp4 12.16 MB
  15. Running Your First Edge App.html 8.18 KB
  16. Exercise Deploy An App at the Edge.html 8.06 KB
  17. IntelScho L1 14 Exercise Deploy An App At The Edge Solution Part 1 V2-X9yI7U2Rn00.mp4 25.96 MB
  17. IntelScho L1 15 Exercise Deploy An App At The Edge Solution Part 2 V2-fvFuMgYUibs.mp4 13.18 MB
  17. IntelScho L1 16 Exercise Deploy An App At The Edge Solution Part 3-0tuVuyD6ffM.mp4 5.41 MB
  17. Solution Deploy An App at the Edge.html 14.68 KB
  18. IntelScho L1 17 Recap-o-fWs0BwbyM.mp4 2.6 MB
  18. Recap.html 7.78 KB
  19. Lesson Glossary.html 12.85 KB
  index.html 5.88 KB
  Part 02-Module 01-Lesson 03_The Model Optimizer
  01. IntelSchol L2 01 Introduction-wxrPEdI9Sj4.mp4 2.51 MB
  01. Introduction.html 7.86 KB
  02. IntelSchol L2 02 The Model Optimizer-Gyj7VPmGOVM.mp4 6.89 MB
  02. The Model Optimizer.html 10.66 KB
  03. IntelSchol L2 03 Optimization Techniques-FKgkB4lSn-U.en.vtt 4.27 KB
  03. IntelSchol L2 03 Optimization Techniques-FKgkB4lSn-U.mp4 11.84 MB
  03. Optimization Techniques.html 12.64 KB
  04. IntelSchol L2 04 Supported Frameworks-5tnzgZuuVsk.mp4 7.51 MB
  04. Supported Frameworks.html 10.45 KB
  05. IntelSchol L2 05 Intermediate Representations-EImZN2nk1t4.mp4 6.94 MB
  05. Intermediate Representations.html 10.4 KB
  06. IntelScho L2 06 Using The Model Optimizer With TensorFlow Models V2-IcZKqDTsp4c.mp4 9.51 MB
  06. Using the Model Optimizer with TensorFlow Models.html 11.02 KB
  07. Exercise Convert a TF Model.html 8.02 KB
  08. IntelSchol L2 07 Exercise Convert A TF Model Solution-_E2EP3ZM92s.mp4 9.68 MB
  08. Solution Convert a TF Model.html 9.21 KB
  09. IntelScho L2 08 Using The Model Optimizer With Caffe Models 2-nUvJ7aXeyMg.mp4 4.92 MB
  09. Using the Model Optimizer with Caffe Models.html 8.92 KB
  10. Exercise Convert a Caffe Model.html 8.03 KB
  11. IntelScho L2 09 Exercise Convert A Caffe Model Solution V2-kfb7BGByUdM.mp4 4.91 MB
  11. Solution Convert a Caffe Model.html 7.88 KB
  12. IntelScho L2 10 Using The Model Optimizer With ONNX Models V2-EeDZn7aeSxM.mp4 4.42 MB
  12. Using the Model Optimizer with ONNX Models.html 9.45 KB
  13. Exercise Convert an ONNX Model.html 8.03 KB
  14. IntelSchol L2 11 Exercise Convert And ONNX Model Solution-KuERfGMco1A.mp4 4.19 MB
  14. Solution Convert an ONNX Model.html 7.8 KB
  15. Cutting Parts of a Model.html 9.15 KB
  15. IntelSchol L2 12 Cutting Parts Of A Model-jhZIkgysCWM.mp4 2.91 MB
  16. IntelSchol L2 13 Supported Layers-bwVlEaAsHvk.mp4 4.11 MB
  16. Supported Layers.html 9.71 KB
  17. Custom Layers.html 9.83 KB
  17. IntelSchol L2 14 Custom Layers-ciSpU0kyjH8.mp4 4.37 MB
  18. Exercise Custom Layers.html 7.98 KB
  19. IntelSchol L2 16 Recap-Zf5zOMg2PjQ.mp4 3.03 MB
  19. Recap.html 7.79 KB
  20. Lesson Glossary.html 11.87 KB
  index.html 5.82 KB
  Part 02-Module 01-Lesson 04_The Inference Engine
  01. IntelScho L3 01 Introduction-BUpkwGhboLg.mp4 2.1 MB
  01. Introduction.html 7.5 KB
  02. IntelScho L3 02 The Inference Engine-dZA4QGbDrs4.mp4 4.93 MB
  02. The Inference Engine.html 9.87 KB
  03. IntelScho L3 03 Supported Devices-m2d1urdJegA.mp4 16.65 MB
  03. Supported Devices.html 10.72 KB
  04. IntelScho L3 04 Using The Inference Engine With An IR-b90ny0AmQF8.mp4 8.17 MB
  04. Using the Inference Engine with an IR.html 12.03 KB
  05. Exercise Feed an IR to the Inference Engine.html 7.76 KB
  06. IntelScho L3 05 Exercise Feed An IR To The Inference Engine Solution-jEmebNVBlc4.en.vtt 5.08 KB
  06. IntelScho L3 05 Exercise Feed An IR To The Inference Engine Solution-jEmebNVBlc4.mp4 8.64 MB
  06. Solution Feed an IR to the Inference Engine.html 10.45 KB
  07. IntelScho L3 06 Sending Interference Requests To The IE-wLN8HYZ05rg.mp4 2.09 MB
  07. Sending Inference Requests to the IE.html 8.79 KB
  08. Asynchronous Requests.html 11.54 KB
  08. IntelScho L3 07 Asynchronous Requests-JGuUIDpn1PY.mp4 3.42 MB
  09. Exercise Inference Requests.html 7.7 KB
  10. IntelScho L3 08 Exercise Inference Requests Solution-QeBpEkkoZ74.en.vtt 5.75 KB
  10. IntelScho L3 08 Exercise Inference Requests Solution-QeBpEkkoZ74.mp4 11.47 MB
  10. Solution Inference Requests.html 8.51 KB
  11. Handling Results.html 9.62 KB
  11. IntelScho L3 09 Handling Results-wO_Io3wDwTM.mp4 3.91 MB
  12. Integrating into Your App.html 8.48 KB
  12. IntelScho L3 10 Integrating Into Your App-vQpLv1Y3pnU.en.vtt 1.76 KB
  12. IntelScho L3 10 Integrating Into Your App-vQpLv1Y3pnU.mp4 3.47 MB
  13. Exercise Integrate into an App.html 7.71 KB
  14. IntelScho L3 11 Integrate Into An App Solution V2-BIdLJkDD5vM.en.vtt 8.49 KB
  14. IntelScho L3 11 Integrate Into An App Solution V2-BIdLJkDD5vM.mp4 17.56 MB
  14. Solution Integrate into an App.html 13.9 KB
  15. Behind the Scenes of Inference Engine.html 11.09 KB
  15. IntelScho L3 12 Behind The Scenes Of Inference Engine-ZWpNQjXSEEc.en.vtt 3.91 KB
  15. IntelScho L3 12 Behind The Scenes Of Inference Engine-ZWpNQjXSEEc.mp4 7.09 MB
  16. IntelScho L3 14 Recap-AVmFgZyk0T0.en.vtt 698 B
  16. IntelScho L3 14 Recap-AVmFgZyk0T0.mp4 2.7 MB
  16. Recap.html 7.55 KB
  17. Lesson Glossary.html 9.08 KB
  index.html 5.58 KB
  Part 02-Module 01-Lesson 05_Deploying an Edge App
  01. IntelScho L4 01 Introduction-IBP4tsdFRPg.en.vtt 1.34 KB
  01. IntelScho L4 01 Introduction-IBP4tsdFRPg.mp4 3.97 MB
  01. Introduction.html 8.09 KB
  02. IntelScho L4 02 OpenCV Basics-c-pyJ3XwWko.en.vtt 3.29 KB
  02. IntelScho L4 02 OpenCV Basics-c-pyJ3XwWko.mp4 5.65 MB
  02. OpenCV Basics.html 11.64 KB
  03. Handling Input Streams.html 9.83 KB
  03. IntelScho L4 03 Handling Input Streams V2-F-7ofR4pdNE.en.vtt 3.36 KB
  03. IntelScho L4 03 Handling Input Streams V2-F-7ofR4pdNE.mp4 7.37 MB
  04. Exercise Handling Input Streams.html 8.1 KB
  05. IntelScho L4 04 Exercise Handing Input Streams Solution V2-p6Hd3dnf-LY.en.vtt 4.83 KB
  05. IntelScho L4 04 Exercise Handing Input Streams Solution V2-p6Hd3dnf-LY.mp4 11.99 MB
  05. Solution Handling Input Streams.html 12.08 KB
  06. Gathering Useful Information from Model Outputs.html 10.03 KB
  06. IntelScho L4 05 Gathering Useful Information For Model Outputs-uNoIZI9bm6U.en.vtt 3.74 KB
  06. IntelScho L4 05 Gathering Useful Information For Model Outputs-uNoIZI9bm6U.mp4 6.23 MB
  07. Exercise Process Model Outputs.html 8.1 KB
  08. IntelScho L4 06 Exercise Process Model Outputs Solution V2-s35d7IvQliE.en.vtt 7.86 KB
  08. IntelScho L4 06 Exercise Process Model Outputs Solution V2-s35d7IvQliE.mp4 17.13 MB
  08. Solution Process Model Outputs.html 9.45 KB
  09. IntelScho L4 07 Intro To MQTT-xe8137UtNCQ.en.vtt 3.61 KB
  09. IntelScho L4 07 Intro To MQTT-xe8137UtNCQ.mp4 8.92 MB
  09. Intro to MQTT.html 9.91 KB
  10. Communicating with MQTT.html 11.06 KB
  10. IntelScho L4 08 Communicating With MQTT-3Jh-akmlmmU.en.vtt 2.82 KB
  10. IntelScho L4 08 Communicating With MQTT-3Jh-akmlmmU.mp4 7.4 MB
  11. IntelScho L4 09 Streaming Images To A Server-MyutnkUqIv4.en.vtt 4.18 KB
  11. IntelScho L4 09 Streaming Images To A Server-MyutnkUqIv4.mp4 7.45 MB
  11. Streaming Images to a Server.html 12.54 KB
  12. Handling Statistics and Images from a Node Server.html 9.16 KB
  12. IntelScho L4 10 Handling Statistics And Images From A Node Server-qHQ9HjmRGWw.en.vtt 1.99 KB
  12. IntelScho L4 10 Handling Statistics And Images From A Node Server-qHQ9HjmRGWw.mp4 4.98 MB
  13. Exercise Server Communications.html 8.1 KB
  14. IntelScho L4 11 Exercise Server Communications Solution V2-c2cNJgrvHmg.en.vtt 8.22 KB
  14. IntelScho L4 11 Exercise Server Communications Solution V2-c2cNJgrvHmg.mp4 22.97 MB
  14. Solution Server Communications.html 11.8 KB
  15. Analyzing Performance Basics.html 11.61 KB
  15. IntelScho L4 12 Analyzing Performance Basics-bGPyVQnLnIw.en.vtt 2.78 KB
  15. IntelScho L4 12 Analyzing Performance Basics-bGPyVQnLnIw.mp4 5.72 MB
  16. IntelScho L4 13 Model Use Cases-rQ0980eYeeA.en.vtt 1.16 KB
  16. IntelScho L4 13 Model Use Cases-rQ0980eYeeA.mp4 1.75 MB
  16. Model Use Cases.html 8.71 KB
  17. Concerning End User Needs.html 9.14 KB
  17. IntelScho L4 14 Concerning End User Needs-aHtne0LexrA.en.vtt 1.3 KB
  17. IntelScho L4 14 Concerning End User Needs-aHtne0LexrA.mp4 2.69 MB
  18. IntelScho L4 15 Recap-6egpCIvzUl8.en.vtt 698 B
  18. IntelScho L4 15 Recap-6egpCIvzUl8.mp4 2.15 MB
  18. Recap.html 8.01 KB
  19. Lesson Glossary.html 9.07 KB
  20. Course Recap.html 9.06 KB
  20. IntelScho L4 16 Course Recap-27C3zdKmt_M.en.vtt 628 B
  20. IntelScho L4 16 Course Recap-27C3zdKmt_M.mp4 2.02 MB
  21. Partner with Intel.html 9.54 KB
  img.zip 118.13 KB
  index.html 5.87 KB
  Part 02-Module 01-Lesson 06_Project Deploy a People Counter App at the Edge
  01. Project Introduction.html 8.98 KB
  02. Project Set-Up.html 7.37 KB
  03. Project Instructions Code.html 11.61 KB
  04. Running the App.html 10.02 KB
  05. Project Instructions Write-Up.html 7.32 KB
  06. Minimum Viable Project.html 7.7 KB
  07. Project Workspace.html 6.68 KB
  Project Description - Deploy a People Counter App at the Edge.html 7.18 KB
  Project Rubric - Deploy a People Counter App at the Edge.html 14.51 KB
  index.html 4.96 KB
  Part 02-Module 01-Lesson 07_Optimize Your GitHub Profile
  01. Prove Your Skills With GitHub.html 11.13 KB
  02. Introduction-Vnj2VNQROtI.ar.vtt 2.28 KB
  02. Introduction-Vnj2VNQROtI.en.vtt 1.58 KB
  02. Introduction-Vnj2VNQROtI.ja-JP.vtt 1.97 KB
  02. Introduction-Vnj2VNQROtI.mp4 9.59 MB
  02. Introduction-Vnj2VNQROtI.pt-BR.vtt 1.79 KB
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  02. Introduction.html 8.05 KB
  03. GitHub profile important items-prvPVTjVkwQ.ar.vtt 3.93 KB
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  03. GitHub profile important items-prvPVTjVkwQ.ja-JP.vtt 3.28 KB
  03. GitHub profile important items-prvPVTjVkwQ.mp4 3.36 MB
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  04. Good GitHub repository-qBi8Q1EJdfQ.ar.vtt 2.56 KB
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  05. Interview with Art - Part 1-ClLYamtaO-Q.ar.vtt 4.59 KB
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  05. Interview with Art - Part 1-ClLYamtaO-Q.mp4 21.79 MB
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  05. Interview with Art - Part 1.html 7.93 KB
  06. Identify fixes for example “bad” profile-AF07y1oAim0.ar.vtt 490 B
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  07. Quick Fixes #1.html 7.76 KB
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  08. Quick Fixes #2.html 8.74 KB
  09. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt 1.5 KB
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  10. Interview with Art - Part 2-Vvzl2J5K7-Y.ar.vtt 2.82 KB
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  10. Interview with Art - Part 2.html 7.92 KB
  11. Commit messages best practices.html 10.11 KB
  12. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt 678 B
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  13. Participating in open source projects-OxL-gMTizUA.ar.vtt 768 B
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  14. Interview with Art - Part 3.html 7.93 KB
  15. Participating in open source projects 2-elZCLxVvJrY.ar.vtt 2.16 KB
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  16. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 B
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  16. Starring interesting repositories-U3FUxkm1MxI.ja-JP.vtt 492 B
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  16. Starring interesting repositories.html 9.45 KB
  17. Next Steps.html 8.07 KB
  Project Description - Optimize Your GitHub Profile.html 10.02 KB
  Project Rubric - Optimize Your GitHub Profile.html 9.88 KB
  img.zip 2.3 MB
  index.html 5.87 KB
  Part 03-Module 01-Lesson 01_Introduction to Hardware at the Edge
  01. Instructor Introduction.html 8.67 KB
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  01. ND131 C02 L00 A01 Intstructor Introduction-SCqbAL7qV2o.mp4 3.81 MB
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  02. Course Overview.html 8.72 KB
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  03. Changes in OpenVINO 2020.1.html 7.61 KB
  04. Lesson Overview.html 7.32 KB
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  05. ND131 C02 L00 A04 Why Is Choosing The Right Hardware Important--GTrOZauP4VA.en.vtt 1.83 KB
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  05. Why is Choosing the Right Hardware Important .html 10.71 KB
  06. Design of Edge AI Systems.html 10.76 KB
  06. ND131 C02 L00 A05 Part 1 Stakeholders-VJNeGT1wHuw.en.vtt 2.12 KB
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  08. Design.html 8.23 KB
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  09. Develop.html 7.56 KB
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  10. ND131 C02 L00 A05 Part 6 Testing And Deployment-qe1hrVLGIls.en.vtt 2.2 KB
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  10. Test and Deploy.html 11.79 KB
  11. Basic Terminology.html 10.65 KB
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  12. Intel DevCloud .html 10.7 KB
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  13. Updating Your Workspace.html 12.25 KB
  14. Walkthrough Using Intel DevCloud.html 7.41 KB
  15. Exercise Using Intel DevCloud.html 7.39 KB
  16. Lesson Review.html 7.27 KB
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  img.zip 340.08 KB
  index.html 5.33 KB
  Part 03-Module 01-Lesson 02_CPUs and Integrated GPUs
  01. Lesson Overview.html 8.25 KB
  01. ND131 C02 L01 A01 Lesson Overview-VyR5gDdPvSk.en.vtt 2.77 KB
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  02. CPU Basics.html 10.55 KB
  03. Threads and Processes.html 11 KB
  04. Multithreading and Multiprocessing.html 12.41 KB
  05. Introduction to Intel Processors.html 14.38 KB
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  07. CPU Specifications (Part 1).html 10.78 KB
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  08. ND131 C02 L01 A04 Applications Of CPU V2-xG7sdR8bldY.en.vtt 5.37 KB
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  10. Updating Your Workspace.html 12.61 KB
  11. Walkthrough CPU and the DevCloud.html 7.77 KB
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  14. Walkthrough IGPU and the DevCloud.html 7.77 KB
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  18. Lesson Review.html 7.65 KB
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  Part 03-Module 01-Lesson 03_VPUs
  01. Lesson Overview.html 7.93 KB
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  02. ND131 C02 L02 A02 Introduction To Vision Processing Units V2-YTsxBng6UMM.en.vtt 2.38 KB
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  04. Myriad X Characteristics.html 10.43 KB
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  05. Intel Neural Compute Stick 2.html 16.35 KB
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  06. Exercise VPU Scenario.html 13.79 KB
  07. Updating Your Workspace.html 12.06 KB
  08. Walkthrough VPU and the DevCloud.html 7.23 KB
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  10. Multi-Device Plugin.html 10.85 KB
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  11. Walkthrough Multi-Device Plugin and the DevCloud.html 7.28 KB
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  13. Lesson Review.html 7.31 KB
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  Part 03-Module 01-Lesson 04_FPGAs
  01. Lesson Overview.html 7.85 KB
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  02. Introduction to FPGAs.html 10.87 KB
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  04. ND131 C02 L03 A05 Converting A Blank FPGA Into An AI Accelerator V2-t6inM0AjpvE.en.vtt 2.93 KB
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  07. Exercise FPGA Scenario.html 15.36 KB
  08. Updating Your Workspace.html 12.1 KB
  09. Walkthrough FPGA and the DevCloud.html 7.27 KB
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  11. Heterogeneous Plugin.html 14.19 KB
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  13. Lesson Review.html 7.31 KB
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  Part 03-Module 01-Lesson 05_Project Smart Queuing System
  01. Project Overview.html 7.54 KB
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  02. Part 1 Hardware Proposal.html 8.74 KB
  03. Scenario 1 Manufacturing.html 11.67 KB
  04. Scenario 2 Retail.html 10.25 KB
  05. Scenario 3 Transportation.html 9.91 KB
  06. Part 2 Testing your Hardware.html 9.83 KB
  07. Step 1 Create the Python Script.html 7.43 KB
  08. Step 2 Create the Job Submission Script.html 7.44 KB
  09. Step 3 Manufacturing Scenario.html 7.41 KB
  10. Step 4 Retail Scenario.html 7.39 KB
  11. Step 5 Transportation Scenario.html 7.41 KB
  12. Step 6 Submit your Project.html 9.79 KB
  Project Description - Smart Queuing System.html 6.7 KB
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  Part 03-Module 01-Lesson 06_Take 30 Min to Improve your LinkedIn
  01. Get Opportunities with LinkedIn.html 11.21 KB
  01. Why Network-exjEm9Paszk.ar.vtt 5.14 KB
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  02. Meet Chris-0ccflD9x5WU.ar.vtt 6.32 KB
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  02. Meet Chris-0ccflD9x5WU.pt-BR.vtt 4.47 KB
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  02. Use Your Story to Stand Out.html 8.75 KB
  03. Elevator Pitch-S-nAHPrkQrQ.ar.vtt 5.13 KB
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  03. Why Use an Elevator Pitch.html 7.37 KB
  04. Create Your Elevator Pitch.html 8.52 KB
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  04. Elevator Pitch-0QtgTG49E9I.pt-BR.vtt 1.94 KB
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  05. Use Your Elevator Pitch on LinkedIn.html 9.77 KB
  06. Create Your Profile With SEO In Mind.html 9.47 KB
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  08. Work Experiences _ Accomplishments.html 9.8 KB
  09. Build and Strengthen Your Network.html 10.65 KB
  10. Reaching Out on LinkedIn.html 9.36 KB
  11. Boost Your Visibility.html 8.58 KB
  12. Up Next.html 8.09 KB
  Project Description - Improve Your LinkedIn Profile.html 8.14 KB
  Project Rubric - Improve Your LinkedIn Profile.html 16.33 KB
  img.zip 3.39 MB
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  Part 04-Module 01-Lesson 01_Introduction to Software Optimization
  01. Instructor Introduction.html 6.92 KB
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  01. ND131 C03 L00 A00 Instructor Introduction-7tRirMjZWDU.mp4 11.02 MB
  02. Course Overview.html 8.52 KB
  02. ND131 C03 L00 A01 Course Overview V3-R-X5m9QQu-E.en.vtt 7.38 KB
  02. ND131 C03 L00 A01 Course Overview V3-R-X5m9QQu-E.mp4 30.61 MB
  03. Installing OpenVINO.html 6.9 KB
  04. Lesson Overview.html 7.37 KB
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  05. ND131 C03 L00 A03 What Is Software Optimization And Why Does It Matter--bl9ypB174Ak.en.vtt 5.82 KB
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  05. ND131 C03 L00 A04 SO Example-k3Ox9gLUkN8.en.vtt 3.44 KB
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  05. What is Software Optimization and Why Does it Matter.html 12.99 KB
  06. ND131 C03 L00 A05 Types Of Software Optimization-H9eHrgsJeuw.en.vtt 7.03 KB
  06. ND131 C03 L00 A05 Types Of Software Optimization-H9eHrgsJeuw.mp4 8.86 MB
  06. Types of Software Optimization.html 17.5 KB
  07. ND131 C03 L00 A06 Performance Metrics - Accuracy-WNl_wDp8e4E.en.vtt 6.39 KB
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  07. ND131 C03 L00 A07 Latency And Throughput-02LPHNsl0Ag.en.vtt 4.58 KB
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  07. Performance Metrics.html 15.88 KB
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  08. Some Other Performance Metrics.html 15.32 KB
  09. ND131 C03 L00 A09 When Do We Do SO-6qerVNayYh8.en.vtt 2.85 KB
  09. ND131 C03 L00 A09 When Do We Do SO-6qerVNayYh8.mp4 4.12 MB
  09. When do we do Software Optimization.html 12.79 KB
  10. Lesson Review.html 6.81 KB
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  Part 04-Module 01-Lesson 02_Reducing Model Operations
  01. Lesson Overview.html 7.77 KB
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  02. Calculating Model FLOPs Dense Layers.html 10.6 KB
  02. ND131 C03 L01 A02 Dense Layer FLOPS V2-3rBzYzAFgL8.en.vtt 4.19 KB
  02. ND131 C03 L01 A02 Dense Layer FLOPS V2-3rBzYzAFgL8.mp4 12.59 MB
  03. Calculating Model FLOPS Convolutional Layers.html 17.49 KB
  03. ND131 C03 L01 A03 Convolutional Layer FLOPS-AMUJFzAfSrE.en.vtt 3.92 KB
  03. ND131 C03 L01 A03 Convolutional Layer FLOPS-AMUJFzAfSrE.mp4 4.59 MB
  04. Calculate the FLOPs in a model.html 13.92 KB
  05. ND131 C03 L01 A04 Pooling Layers-SV5Xrmrhs-Q.en.vtt 3.72 KB
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  05. Using Efficient Layers Pooling Layers.html 10.63 KB
  06. Exercise Pooling Performance.html 7.28 KB
  07. ND131 C03 L01 A06 Separable Convolution-6Mo5Jb7TWT4.en.vtt 5.21 KB
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  07. Using Efficient Layers Separable Convolutions.html 18.88 KB
  08. Exercise Separable Convolutions Performance.html 7.31 KB
  09. Measuring Layerwise Performance.html 7.28 KB
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  11. Model Pruning.html 13.75 KB
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  Part 04-Module 01-Lesson 03_Reducing Model Size
  01. Lesson Overview.html 7.59 KB
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  02. Introduction to Quantization.html 15.24 KB
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Description


Lynda and other Courses >>> https://www.freecoursesonline.me/
For Developer Tools & Apps >>> https://ftuapps.com/
Forum for discussion >>> https://1hack.us/




Nanodegree Program–nd131
Publisher : Udacity
Subtitle : English CC Included
Updated : July 2020
Course Source : https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131

Intel® Edge AI for IoT Developers

Lead the development of cutting-edge Edge AI applications for the future of the Internet of Things. Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision & deep learning inference applications.

ESTIMATED TIME
- 3 Months
- At 10 hours / week

PREREQUISITES
- Intermediate Python, and Experience with Deep Learning, Command Line, and OpenCV

In collaboration with



What You Will Learn

Intel® Edge AI for IoT Developers

Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications, and run pre-trained deep learning models for computer vision on-premise. You will identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU), and utilize the Intel® DevCloud for the Edge to test model performance on the various hardware types. Finally, you will use software tools to optimize deep learning models to improve performance of Edge AI systems.

PREREQUISITE KNOWLEDGE

This program requires intermediate knowledge of Python, and experience with Deep Learning, Command Line, and OpenCV.

• Edge AI Fundamentals with OpenVINO™

Leverage a pre-trained model for computer vision inferencing. You will convert pre-trained models into the framework agnostic intermediate representation with the Model Optimizer, and perform efficient inference on deep learning models through the hardware-agnostic Inference Engine. Finally, you will deploy an app on the edge, including sending information through MQTT, and analyze model performance and use cases

DEPLOY A PEOPLE COUNTER AT THE EDGE

• Hardware for Computer Vision & Deep Learning Application Deployment

Grow your expertise in choosing the right hardware. Identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU). Utilize the Intel® DevCloud for the Edge to test model performance and deploy power-efficient deep neural network inference on on the various hardware types. Finally, you will distribute workload on available compute devices in order to improve model performance.

DESIGN A SMART QUEUING SYSTEM

• Optimization Techniques and Tools for Computer Vision & Deep Learning Applications

Learn how to optimize your model and application code to reduce inference time when running your model at the edge. Use different software optimization techniques to improve the inference time of your model. Calculate how computationally expensive your model is. Use the DL Workbench to optimize your model and benchmark the performance of your model. Use a VTune amplifier to find and fix hotspots in your application code. Finally, package your application code and data so that it can be easily deployed to multiple devices.

BUILD A COMPUTER POINTER CONTROLLER

Learn with the best

Stewart Christie, Michael Virgo, Soham Chatterjee, Vaidheeswaran Archana

GET STARTED WITH

Intel® Edge AI for IoT Developers

LEARN
• Lead the development of cutting-edge Edge AI applications that are the future of the Internet of Things.

AVERAGE TIME
• On average, successful students take 3 months to complete this program.

BENEFITS INCLUDE
• Real-world projects from industry experts
• Technical mentor support
• Personal career coach & career services

STAY SHARP WHILE STAYING IN
• Financial support available worldwide to help in this challenging time
• Spend your time at home learning new, higher-paying job skills
• Commit to a brighter future by learning today

Why should I enroll?

70% of data being created is at the edge, and only half of that will go to the public cloud; the rest will be stored and processed at the edge, which requires a different kind of developer. Demand for professionals with the Edge AI skills will be immense, as the Edge Artificial Intelligence (AI) software market size is forecasted to grow from $355 Million in 2018, to $1.15 billion by 2023, at an Annual Growth Rate of 27%.(MarketsandMarkets) In the Edge AI for IoT Developers Nanodegree program, you'll leverage the potential of edge computing and use the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications.
Computer Vision is a fast-growing technology being deployed in nearly every industry from factory floors to amusement parks to shopping malls, smart buildings, and smart homes. It is also driving the evolution of machine learning and human interactions with intelligent systems. Additional applications include drones, security cameras, robots, facial recognition on cell phones, self-driving vehicles, and more, which means these industries and more all need developers with computer vision and deep learning IoT experience.

Read more at course page.