Tiny machine learning quickstart par salerno simone (8 résultats)

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New. In English.

Taschenbuch. Etat : Neu. Tiny Machine Learning Quickstart | Machine Learning for Arduino Microcontrollers | Simone Salerno | Taschenbuch | Maker Innovations Series | xx | Englisch | 2025 | Apress | EAN 9798868812934 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Etat : new. Questo è un articolo print on demand.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform.You'll start by exploring Machine learning in the context of embedded, resource-constrained devices as opposed to your powerful, gigabyte-RAM computer. You'll review the unique challenges it poses, but also the limitless possibilities it opens. Next, you'll work through nine projects that encompass different data types (tabular, time series, audio and images) and tasks (classification and regression). Each project comes with tips and tricks to collect, load, plot and analyse each type of data.Throughout the book, you'll apply three different approaches to TinyML: traditional algorithms (Decision Tree, Logistic Regression, SVM), Edge Impulse (a no-code online tools), and TensorFlow for Microcontrollers. Each has its strengths and weaknesses, and you will learn how to choose the most appropriate for your use case. TinyML Quickstart will provide a solid reference for all your future projects with minimal cost and effort.What You Will LearnNavigate embedded ML challengesIntegrate Python with Arduino for seamless data processingImplement ML algorithmsHarness the power of Tensorflow for artificial neural networksLeverage no-code tools like Edge ImpulseExecute real-world projectsWho This Book Is ForElectronics hobbyists and developers with a basic understanding of Tensorflow, ML in Python, and Arduino-based programming looking to apply that knowledge with microcontrollers. Previous experience with C++ is helpful but not required. 326 pp. Englisch.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform.You'll start by exploring Machine learning in the context of embedded, resource-constrained devices as opposed to your powerful, gigabyte-RAM computer. You'll review the unique challenges it poses, but also the limitless possibilities it opens. Next, you'll work through nine projects that encompass different data types (tabular, time series, audio and images) and tasks (classification and regression). Each project comes with tips and tricks to collect, load, plot and analyse each type of data.Throughout the book, you'll apply three different approaches to TinyML: traditional algorithms (Decision Tree, Logistic Regression, SVM), Edge Impulse (a no-code online tools), and TensorFlow for Microcontrollers. Each has its strengths and weaknesses, and you will learn how to choose the most appropriate for your use case. TinyML Quickstart will provide a solid reference for all your future projects with minimal cost and effort.What You Will LearnNavigate embedded ML challengesIntegrate Python with Arduino for seamless data processingImplement ML algorithmsHarness the power of Tensorflow for artificial neural networksLeverage no-code tools like Edge ImpulseExecute real-world projectsWho This Book Is ForElectronics hobbyists and developers with a basic understanding of Tensorflow, ML in Python, and Arduino-based programming looking to apply that knowledge with microcontrollers. Previous experience with C++ is helpful but not required.…

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform.You'll start by exploring Machine learning in the context of embedded, resource-constrained devices as opposed to your powerful, gigabyte-RAM computer. You'll review the unique challenges it poses, but also the limitless possibilities it opens. Next, you'll work through nine projects that encompass different data types (tabular, time series, audio and images) and tasks (classification and regression). Each project comes with tips and tricks to collect, load, plot and analyse each type of data.Throughout the book, you'll apply three different approaches to TinyML: traditional algorithms (Decision Tree, Logistic Regression, SVM), Edge Impulse (a no-code online tools), and TensorFlow for Microcontrollers. Each has its strengths and weaknesses, and you will learn how to choose the most appropriate for your use case. TinyML Quickstart will provide a solid reference for all your future projects with minimal cost and effort.What You Will LearnNavigate embedded ML challengesIntegrate Python with Arduino for seamless data processingImplement ML algorithmsHarness the power of Tensorflow for artificial neural networksLeverage no-code tools like Edge ImpulseExecute real-world projectsWho This Book Is ForElectronics hobbyists and developers with a basic understanding of Tensorflow, ML in Python, and Arduino-based programming looking to apply that knowledge with microcontrollers. Previous experience with C++ is helpful but not required.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 348 pp. Englisch.…