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DeepLearning
  • Home
    • Physics in DL
      • Boltzmann Machine
      • Digital Twin Phy
    • jkEvents
      • PRIST University
      • IETEevent
      • Baranovichi
        • Model
          • TF Model
          • DLtrain Model
        • Data-Set
          • IBM Cloud Object Storage
          • MNIST++
        • Train DL Model
          • CoLab
          • TF On Prime CPU
          • Build DLtrain in Power 9
          • Build DLtrain for jetson Nano
          • Build DLtrain in Docker x86
          • DLtrain in Windows M/C
          • DLtrain On Prime CPU
        • Save and Load
        • Inference
          • Flask Micro Service
          • Inference via JavaScript
          • Deploy in Cloud
          • Android Phone
          • Jetson Nano TF Model
          • Jetson Nano DLtrain
          • Near Edge
          • DL Model Update
      • Sambhram
        • Digital Twin
        • Edge Ready
        • BO
        • Startups
        • MBA induction Y22
      • CV Raman Global Univ
        • Thanks
        • Microgrid
        • AI in Monitoring MG
        • IoT sensors
        • Interpret Measurements
        • Edge in Microgrid
        • DLtrain in Jetson Nano
        • AI in Smart phone
        • Deploy AI in Sensors
        • Thank You
      • Dayananda Sagar
    • jkhome
      • Share
  • DL in IoT Edge
    • Edge Infra
      • Compute via CUDA
        • Introduction
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    • Open Power
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      • Nalla Paru
      • Ventilator
        • Biocontainment
        • Auscultation
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    • Home
      • Physics in DL
        • Boltzmann Machine
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      • jkEvents
        • PRIST University
        • IETEevent
        • Baranovichi
          • Model
            • TF Model
            • DLtrain Model
          • Data-Set
            • IBM Cloud Object Storage
            • MNIST++
          • Train DL Model
            • CoLab
            • TF On Prime CPU
            • Build DLtrain in Power 9
            • Build DLtrain for jetson Nano
            • Build DLtrain in Docker x86
            • DLtrain in Windows M/C
            • DLtrain On Prime CPU
          • Save and Load
          • Inference
            • Flask Micro Service
            • Inference via JavaScript
            • Deploy in Cloud
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            • Jetson Nano TF Model
            • Jetson Nano DLtrain
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          • Digital Twin
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        • CV Raman Global Univ
          • Thanks
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          • AI in Monitoring MG
          • IoT sensors
          • Interpret Measurements
          • Edge in Microgrid
          • DLtrain in Jetson Nano
          • AI in Smart phone
          • Deploy AI in Sensors
          • Thank You
        • Dayananda Sagar
      • jkhome
        • Share
    • DL in IoT Edge
      • Edge Infra
        • Compute via CUDA
          • Introduction
          • Vector Add in CUDA
          • MAT Mul in CUDA
            • tst1.py
            • tst2.py
            • tst3.py
            • tst4.py
          • Reference
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        • Setting Up
          • Resources
          • Installation
          • First Boot
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          • Internet Access via Wi-Fi
          • RDP / SSH
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          • nvcc
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          • Python
        • Quick Demo
        • Hello World
        • Kernel Update
      • J7 App
        • Android SDK
        • Android NDK
        • J7 Source Code
        • Credential Update
        • J7Build
        • Generate APK
        • Install APK in Phone
        • Using J7
      • IBM Watson VR
        • Watson AI
        • Watson Studio
        • Watson VR service
        • Custom Model
        • Train
        • Test
        • Deploy
        • Client APP
        • Recap
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        • Part 1
        • Part 2
        • Part 3
      • Open Power
        • A2O
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        • sports
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Visual Recognition in Agriculture

IoT in 5G Network – Research by Jayakumar. S PhD – Part 3 - TECHx MediaInnovation in creating optimal yet robust models by using deep learning convolutional neural network has led to low cost “customized

Case Study: Monitor Tomato Farm “Customization ready Visual Recognition Micro service”


Plant Disease Detection Using Convolutional Neural Networks and PyTorchMachine learning, Deep learning, and Artificial intelligence are the Future. We use these technologies in almost every field. In the…
One Teacher able to handle not more than 40 students, But Power AC922 expected to handle 2000 plus CUDA cores and 100 plus Tensor Cores, when AI workload assigned to Power AC922.
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