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  • 1
    Online Resource
    Online Resource
    TinyML : wykorzystanie TensorFlow Lite do uczenia maszynowego na Arduino i innych mikrokontrolerach (2022)
    Gliwice : Helion
    Details
    ISBN: 9788328383630 , 8328383632
    Language: Polish
    Pages: 1 online resource (432 pages) , illustrations
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: TensorFlow ; Machine learning ; Signal processing Digital techniques ; Microcontrollers
    Abstract: Może się wydawać, że profesjonalne systemy uczenia maszynowego wymagają sporych zasobów mocy obliczeniowej i energii. Okazuje się, że niekoniecznie: można tworzyć zaawansowane, oparte na sieciach neuronowych aplikacje, które doskonale poradzą sobie bez potężnych procesorów. Owszem, praca na mikrokontrolerach podobnych do Arduino lub systemach wbudowanych wymaga pewnego przygotowania i odpowiedniego podejścia, jest to jednak fascynujący sposób na wykorzystanie niewielkich urządzeń o niskim zapotrzebowaniu na energię do tworzenia zdumiewających projektów. Ta książka jest przystępnym wprowadzeniem do skomplikowanego świata, w którym za pomocą techniki TinyML wdraża się głębokie uczenie maszynowe w systemach wbudowanych. Nie musisz mieć żadnego doświadczenia z zakresu uczenia maszynowego czy pracy z mikrokontrolerami. W książce wyjaśniono, jak można trenować modele na tyle małe, by mogły działać w każdym środowisku - również Arduino. Dokładnie opisano sposoby użycia techniki TinyML w tworzeniu systemów wbudowanych opartych na zastosowaniu ucze nia maszynowego. Zaprezentowano też kilka ciekawych projektów, na przykład dotyczący budowy urządzenia rozpoznającego mowę, magicznej różdżki reagującej na gesty, a także rozszerzenia możliwości kamery o wykrywanie ludzi.
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    AI at the Edge (2022)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (29 pages)
    Edition: 1st edition
    Keywords: Electronic books
    Abstract: Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target—from ultra-low power microcontrollers to flexible embedded Linux devices—for applications that reduce latency, protect privacy, and work without a network connection, greatly expanding the capabilities of the IoT. This practical guide gives engineering professionals and product managers an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You’ll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level roadmap will help you get started. Develop your expertise in artificial intelligence and machine learning on edge devices Understand which projects are best solved with edge AI Explore typical design patterns used with edge AI apps Use an iterative workflow to develop an edge AI application Optimize models for deployment to embedded devices Improve model performance based on feedback from real-world use
    Note: Online resource; Title from title page (viewed December 25, 2022) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    Online Resource
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  • 3
    Online Resource
    Online Resource
    TinyML (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (350 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size—small enough to work on the digital signal processor in an Android phone. With this practical book, you’ll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware. Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary. Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms Understand how to work with Arduino and ultralow-power microcontrollers Use techniques for optimizing latency, energy usage, and model and binary size
    Note: Online resource; Title from title page (viewed December 25, 2019)
    URL: lizenzpflichtig
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    Online Resource
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  • 4
    Online Resource
    Online Resource
    TinyML : li yu TensorFlow Lite zai Arduino he chao di gong hao wei kong zhi qi shang bu shu ji qi xue xi = TinyML : machine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers (2020)
    Beijing： : Ji xie gong ye chu ban she
    Details
    Orig.schr. Ausgabe: 第1版.
    Title: TinyML : : 基于TensorFlow Lite 在 Arduino 和超低功耗微控制器上部署机器学习 = TinyML : machine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers /
    Publisher: 机械工业出版社,
    Language: Chinese
    Pages: 1 online resource (434 pages) , illustrations.
    Edition: Di 1 ban.
    Series Statement: O'Reilly jing pin tu shu xi lie
    DDC: 006.31
    Keywords: TensorFlow ; Machine learning ; Signal processing Digital techniques ; Microcontrollers ; Apprentissage automatique ; Traitement du signal ; Techniques numériques ; Microcontrôleurs ; Machine learning ; Microcontrollers ; Signal processing ; Digital techniques ; Electronic books
    Abstract: Detailed summary in vernacular field.
    Note: 880-05;"Hua zhang IT."
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
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