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TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
Price: $36.87
(as of Dec 28,2024 15:38:41 UTC – Details)
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O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
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 MicrocontrollersTinyML, 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
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Mic
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Mic
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TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power MicTinyML, or Tiny Machine Learning, is a growing field that focuses on running machine learning models on very small, low-power devices. One popular platform for TinyML is Arduino, a popular open-source electronics platform that is widely used for building DIY projects and prototypes.
With the help of TensorFlow Lite, a lightweight version of Google’s TensorFlow machine learning framework, it is now possible to run machine learning models on Arduino boards. This allows for powerful AI capabilities on devices that have limited processing power and memory.
By combining TensorFlow Lite with ultra-low-power microphones, it is possible to create projects that can recognize sounds, words, and even gestures with high accuracy. This opens up a whole new world of possibilities for IoT devices, wearables, and other applications that require machine learning capabilities in a small and power-efficient package.
If you are interested in exploring the exciting world of TinyML with TensorFlow Lite on Arduino and ultra-low-power microphones, there are plenty of resources available online to help you get started. From tutorials and sample projects to community forums and support groups, the TinyML community is vibrant and welcoming to newcomers.
So why wait? Dive into the world of TinyML today and start building amazing AI-powered projects with TensorFlow Lite on Arduino and ultra-low-power microphones. The possibilities are endless!
#TinyML #Machine #Learning #TensorFlow #Lite #Arduino #UltraLowPower #Mic,machine learning
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
Price:$49.99– $31.51
(as of Dec 17,2024 16:28:40 UTC – Details)
From the brand
Explore our collection
Sharing the knowledge of experts
O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
Publisher : O’Reilly Media; 1st edition (January 21, 2020)
Language : English
Paperback : 501 pages
ISBN-10 : 1492052043
ISBN-13 : 978-1492052043
Item Weight : 1.75 pounds
Dimensions : 7.01 x 1.01 x 9.17 inchesCustomers say
Customers find the book’s explanations thorough and valuable. They appreciate the concise and no-fluff approach that combines theory and hands-on learning. Readers describe the book as great, well-written, and entertaining for developers of all levels.
AI-generated from the text of customer reviews
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power MicrocontrollersTinyML, or Tiny Machine Learning, is a growing field that focuses on running machine learning models on ultra-low-power devices, such as microcontrollers. With the advancements in hardware and software, it is now possible to deploy TensorFlow Lite models on Arduino boards and other microcontrollers, enabling edge computing and real-time inference on small, battery-powered devices.
In this post, we will explore the possibilities of running machine learning models on Arduino using TensorFlow Lite. We will discuss the benefits of using ultra-low-power microcontrollers for machine learning tasks, the challenges of deploying models on resource-constrained devices, and the tools and libraries available for developing and deploying TinyML applications.
Stay tuned for more updates on TinyML and how you can leverage the power of machine learning on Arduino and other ultra-low-power microcontrollers.
#TinyML #Machine #Learning #TensorFlow #Lite #Arduino #UltraLowPower #Microcontrollers
TinyML : Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power…
TinyML : Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power…
Price : 9.99
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TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power DevicesIn the world of machine learning, there is a growing trend towards developing models that can be deployed on small, low-power devices. This is where TinyML comes in – a field that focuses on running machine learning algorithms on microcontrollers and other ultra-low-power devices.
One of the key technologies driving the development of TinyML is TensorFlow Lite, a lightweight version of Google’s popular machine learning framework, TensorFlow. With TensorFlow Lite, developers can train and deploy models on devices with limited computational resources, such as Arduino boards and other microcontrollers.
By harnessing the power of TensorFlow Lite, developers can create applications that leverage the capabilities of machine learning on devices that were previously unable to support such functionality. This opens up a world of possibilities for applications in IoT, wearables, and other areas where power consumption and size constraints are critical.
In this post, we will explore the exciting world of TinyML and how TensorFlow Lite is revolutionizing machine learning on ultra-low-power devices. Stay tuned for more insights and tutorials on how to get started with TinyML and TensorFlow Lite on your own projects.
#TinyML #Machine #Learning #TensorFlow #Lite #Arduino #UltraLowPower..
