TensorFlow Lite Training in San Francisco Bay Area
Online or onsite, instructor-led live TensorFlow Lite training courses demonstrate through interactive hands-on practice how to use TensorFlow Lite to to deploy deep learning models on embedded devices.
TensorFlow Lite training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live TensorFlow Lite trainings in San Francisco Bay Area can be carried out locally on customer premises or in NobleProg corporate training centers.
TensorFlow Lite is also known as Google's TensorFlow Lite.
NobleProg -- Your Local Training Provider
CA, Sunnyvale - Downtown Sunnyvale
640 W California Ave Suite 210, Sunnyvale, united states, 94086
The Sunnyvale center is located one block from Caltrain rail station and provides easy access to Central Expressway and High...
The Sunnyvale center is located one block from Caltrain rail station and provides easy access to Central Expressway and Highway 101.
It is in walking distance to the city's downtown district, featuring numerous restaurants, shops, and businesses, as well as a short drive from San Jose International Airport.
CA, San Francisco - Golden Gate
75 Broadway, San Francisco , united states, 94111
With its fabulous arched windows, the 75 Broadway office, located in the heart of Jackson Square, is designed to impress. Sit...
With its fabulous arched windows, the 75 Broadway office, located in the heart of Jackson Square, is designed to impress. Situated at Golden Gateway Commons on the second floor of this unique campus-style building, it overlooks the green terrain's of Sydney Walton Park with the famous waterfront piers behind. This enviable location is a short walk from public transport and just blocks away from the Embarcadero and the ferry building, as well as a host of convenient amenities.
This instructor-led, live training in San Francisco Bay Area (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.
This instructor-led, live training in San Francisco Bay Area (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.
This instructor-led, live training in San Francisco Bay Area (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.
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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