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    TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter Gian Marco Iodice, Ronan Naughton

    (ebook) (audiobook) (audiobook) Książka w języku 1
    TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter Gian Marco Iodice, Ronan Naughton - okladka książki

    TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter Gian Marco Iodice, Ronan Naughton - okladka książki

    TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter Gian Marco Iodice, Ronan Naughton - audiobook MP3

    TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter Gian Marco Iodice, Ronan Naughton - audiobook CD

    Autorzy:
    Gian Marco Iodice, Ronan Naughton
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    344
    Dostępne formaty:
         PDF
         ePub
    This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers.
    The 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, supplying power to microcontrollers with batteries, and more. Next, 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. Later, 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 that will help you step up your TinyML game.
    By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.

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    O autorze książki

    Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones.

    Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs.

    In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.

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