nach der Bestellung gedruckt Neuware - Printed after ordering - Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT dataKey Features:Discover quick solutions to common problems that you'll face while building smart IoT applicationsImplement advanced techniques such as computer vision, NLP, and embedded machine learningBuild, maintain, and deploy machine learning systems to extract key insights from IoT dataBook description:Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users' lives easier. With this AI cookbook, you'll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications.Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You'll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you'll learn how to deploy models and improve their performance with ease.By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.What You Will Learn:Explore various AI techniques to build smart IoT solutions from scratchUse machine learning and deep learning techniques to build smart voice recognition and facial detection systemsGain insights into IoT data using algorithms and implement them in projectsPerform anomaly detection for time series data and other types of IoT dataImplement embedded systems learning techniques for machine learning on small devicesApply pre-trained machine learning models to an edge deviceDeploy machine learning models to web apps and mobile using TensorFlow.js and JavaWho this book is for:�If you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. Knowledge of the Python programming language and basic IoT concepts is required to grasp the concepts covered in this artificial intelligence book more effectively.

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Detalles bibliogr�ficos

T�tulo: Artificial Intelligence for IoT Cookbook : Over 70 recipes for building AI solutions for smart homes, industrial IoT, and smart cities
Editor: Packt Publishing
Fecha de publicaci�n: 2021
Idioma: Ingl�s
ISBN 10: 1838981985
ISBN 13: 9781838981983
Encuadernaci�n: Taschenbuch
Estado: Neu

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Sinopsis

Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT data

Key Features:

Discover quick solutions to common problems that you'll face while building smart IoT applications
Implement advanced techniques such as computer vision, NLP, and embedded machine learning
Build, maintain, and deploy machine learning systems to extract key insights from IoT data

Book description:

Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users' lives easier. With this AI cookbook, you'll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications.

Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You'll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you'll learn how to deploy models and improve their performance with ease.

By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.

What You Will Learn:

Explore various AI techniques to build smart IoT solutions from scratch
Use machine learning and deep learning techniques to build smart voice recognition and facial detection systems
Gain insights into IoT data using algorithms and implement them in projects
Perform anomaly detection for time series data and other types of IoT data
Implement embedded systems learning techniques for machine learning on small devices
Apply pre-trained machine learning models to an edge device
Deploy machine learning models to web apps and mobile using TensorFlow.js and Java

Who this book is for:

If you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. Knowledge of the Python programming language and basic IoT concepts is required to grasp the concepts covered in this artificial intelligence book more effectively.

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Michael Roshak is a cloud architect and strategist with extensive subject matter expertise in enterprise cloud transformation programs and infrastructure modernization through designing, and deploying cloud-oriented solutions and architectures. He is responsible for providing strategic advisory for cloud adoption, consultative technical sales, and driving broad cloud services consumption with highly strategic accounts across multiple industries.

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