TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
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- Nombre de pages320
- PrésentationBroché
- FormatGrand Format
- Poids0.643 kg
- Dimensions19,1 cm × 23,5 cm × 1,9 cm
- ISBN978-1-80181-497-3
- EAN9781801814973
- Date de parution01/04/2022
- ÉditeurPackt Publishing
- PréfacierRonan Naughton
Résumé
This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make Al ubiquitous with extremely low-powered devices, such as microcontrollers. TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico.
As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button and supplying power to microcontrollers with batteries. After that, you'll cover recipes relating to temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios.
Then, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU, which will help you step up your TinyML game. By the end of this book, you'll be well versed in best practices and machine learning frameworks to develop machine learning apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button and supplying power to microcontrollers with batteries. After that, you'll cover recipes relating to temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios.
Then, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU, which will help you step up your TinyML game. By the end of this book, you'll be well versed in best practices and machine learning frameworks to develop machine learning apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make Al ubiquitous with extremely low-powered devices, such as microcontrollers. TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico.
As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button and supplying power to microcontrollers with batteries. After that, you'll cover recipes relating to temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios.
Then, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU, which will help you step up your TinyML game. By the end of this book, you'll be well versed in best practices and machine learning frameworks to develop machine learning apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button and supplying power to microcontrollers with batteries. After that, you'll cover recipes relating to temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios.
Then, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU, which will help you step up your TinyML game. By the end of this book, you'll be well versed in best practices and machine learning frameworks to develop machine learning apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.