Embedded Artificial Intelligence Devices (24 results)
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Paperback or Softback. Condition: New. TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter. Book.
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Embedded Artificial Intelligence : Devices, Embedded Systems, and Industrial Applications
Vermesan, Ovidiu (EDT); Nava, Mario Diaz (EDT); Debaillie, Björn (EDT)
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Embedded Artificial Intelligence : Devices, Embedded Systems, and Industrial Applications
Vermesan, Ovidiu (EDT); Nava, Mario Diaz (EDT); Debaillie, Björn (EDT)
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Embedded Artificial Intelligence : Devices, Embedded Systems, and Industrial Applications
Vermesan, Ovidiu (EDT); Nava, Mario Diaz (EDT); Debaillie, Björn (EDT)
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Embedded Artificial Intelligence : Devices, Embedded Systems, and Industrial Applications
Vermesan, Ovidiu (EDT); Nava, Mario Diaz (EDT); Debaillie, Björn (EDT)
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Embedded Artificial Intelligence: Devices, Embedded Systems, and Industrial Applications
Vermesan, Ovidiu (Editor)/ Nava, Mario Diaz (Editor)/ Debaillie, Björn (Editor)
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. TinyML Cookbook | Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter | Gian Marco Iodice | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | Packt Publishing | EAN 9781801814973 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 B…ad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
- Softcover
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Work through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learningKey FeaturesTrain and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi PicoWor…k with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge ImpulseExplore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPUBook DescriptionThis 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.What you will learnUnderstand the relevant microcontroller programming fundamentalsWork with real-world sensors such as the microphone, camera, and accelerometerRun on-device machine learning with TensorFlow Lite for MicrocontrollersImplement an app that responds to human voice with Edge ImpulseLeverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE SenseCreate a gesture-recognition app with Raspberry Pi PicoDesign a CIFAR-10 model for memory-constrained microcontrollersRun an image classifier on a virtual Arm Ethos-U55 microNPU with microTVMWho this book is forThis book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.Table of ContentsGetting Started with TinyMLPrototyping with MicrocontrollersBuilding a Weather Station with TensorFlow Lite for MicrocontrollersVoice Controlling LEDs with Edge ImpulseIndoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino NanoBuilding a Gesture-Based Interface for YouTube PlaybackRunning a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OSToward the Next TinyML Generation with microNPU.
- Hardcover
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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- Hardcover
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recent technological developments in sensors, edge computing, connectivity, and artificial intelligence (AI) technologies have accelerated the integration of data analysis based on embedded AI capabilities into resource-constrained, energy-effi…cient hardware devices for processing information at the network edge.Embedded AI combines embedded machine learning (ML) and deep learning (DL) based on neural networks (NN) architectures such as convolutional NN (CNN), or spiking neural network (SNN) and algorithms on edge devices and implements edge computing capabilities that enable data processing and analysis without optimised connectivity and integration, allowing users to access data from various sources.Embedded AI efficiently implements edge computing and AI processes on resource-constrained devices to mitigate downtime and service latency, and it successfully merges AI processes as a pivotal component in edge computing and embedded system devices. Embedded AI also enables users to reduce costs, communication, and processing time by assembling data and by supporting user requirements without the need for continuous interaction with physical locations.This book provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO.The book's content targets researchers, designers, developers, academics, post-graduate students and practitioners seeking recent research on embedded AI. It combines the latest developments in embedded AI, addressing methodologies, tools, and techniques to offer insight into technological trends and their use across different industries.
