Garden Court South Beach

Durban, South Africa

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TensorFlow Lite for Embedded Linux

 21 Hours
This instructor-led, live training in Durban (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices. By the end of this training, participants will be able to:
  • Install and configure Tensorflow Lite on an embedded device.
  • Understand the concepts and components underlying TensorFlow Lite.
  • Convert existing models to TensorFlow Lite format for execution on embedded devices.
  • Work within the limitations of small devices and TensorFlow Lite, while learning how to expand the scope of operations that can be run.
  • Deploy a deep learning model on an embedded device running Linux.
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TensorFlow Lite for Android

 21 Hours
This instructor-led, live training in Durban (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities. By the end of this training, participants will be able to:
  • Install and configure TensorFlow Lite.
  • Understand the principles behind TensorFlow, machine learning and deep learning.
  • Load TensorFlow Models onto an Android device.
  • Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
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Tensorflow Lite for Microcontrollers

 21 Hours
This instructor-led, live training in Durban (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices. By the end of this training, participants will be able to:
  • Install TensorFlow Lite.
  • Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
  • Add AI to hardware devices without relying on network connectivity.
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TensorFlow Lite for iOS

 21 Hours
This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities. By the end of this training, participants will be able to:
  • Install and configure TensorFlow Lite.
  • Understand the principles behind TensorFlow and machine learning on mobile devices.
  • Load TensorFlow Models onto an iOS device.
  • Run an iOS application capable of detecting and classifying an object captured through the device's camera.
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