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ASIN : B082TY3SX7
Publisher : O’Reilly Media; 1st edition (December 16, 2019)
Publication date : December 16, 2019
Language : English
File size : 24520 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 506 pages
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
TinyML, or Tiny Machine Learning, refers to the practice of running machine learning algorithms on ultra-low-power microcontrollers. This allows for the deployment of intelligent applications in small, resource-constrained devices such as wearables, IoT devices, and even smart sensors.
One of the most popular frameworks for implementing TinyML is TensorFlow Lite, a lightweight version of Google’s TensorFlow machine learning library. By using TensorFlow Lite on microcontrollers like Arduino, developers can leverage the power of machine learning for edge computing applications.
With TensorFlow Lite, developers can train machine learning models on more powerful hardware and then deploy them on microcontrollers for inference at the edge. This enables real-time decision-making without needing to rely on a constant internet connection or cloud computing resources.
Using TensorFlow Lite on Arduino and other ultra-low-power microcontrollers opens up a world of possibilities for creating intelligent devices that can perform tasks like image recognition, speech recognition, anomaly detection, and more right on the device itself.
Overall, TinyML with TensorFlow Lite on Arduino and ultra-low-power microcontrollers is a game-changer for the world of embedded systems and IoT, allowing for the creation of intelligent devices that are efficient, responsive, and capable of running complex machine learning algorithms in a small and power-efficient package.
#TinyML #Machine #Learning #TensorFlow #Lite #Arduino #UltraLowPower #Microcontrollers
