Tiny machine learning tensorflow de warden pete (3 resultados)

Autor
Título
Refinar con la Búsqueda avanzada

Filtrar la búsqueda

  • Libros (3)

a

Intervalo de precios personalizado (EUR)

a

  • Idioma: Inglés

    Editorial: O'Reilly Media, 2020

    1492052043 / 9781492052043

    • Tapa blanda

    Librería: World of Books Inc, Montgomery, IL, Estados Unidos de AmericaWorld of Books Inc

    Vendedor de 2 estrellas
    Contactar con el vendedor

    Condición: Usado - Aceptable

    EUR 15,79

     Gastos de envío gratis 
    Se envía dentro de Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Paperback. Condición: Good. Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size--small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. As of early 2022, the supplemental code files are available at https: //oreil.ly/XuIQ4. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google's toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size.

  • Idioma: Inglés

    Editorial: O'reilly Media Jan 2020, 2020

    1492052043 / 9781492052043

    • Tapa blanda

    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 78,76

    Envío por EUR 30,50 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Taschenbuch. Condición: Neu. Neuware - Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in sizesmall enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.- Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures- Work with Arduino and ultra-low-power microcontrollers- Learn the essentials of ML and how to train your own models- Train models to understand audio, image, and accelerometer data- Explore TensorFlow Lite for Microcontrollers, Google's toolkit for TinyML- Debug applications and provide safeguards for privacy and security- Optimize latency, energy usage, and model and binary size.

  • Idioma: Inglés

    Editorial: O'Reilly Media, 2020

    1492052043 / 9781492052043

    • Tapa blanda

    Librería: preigu, Osnabrück, Alemaniapreigu

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 51,15

    Envío por EUR 70,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Taschenbuch. Condición: Neu. Tiny ML | Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers | Pete Warden | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | O'Reilly Media | EAN 9781492052043 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.