Beginning Machine Learning In The Browser Quickstart Guide To Gait Analysis With Javascript And Tensorflowjs
Nagender Kumar Suryadevara
Resumo
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Beginning Machine Learning In The Browser Quickstart Guide To Gait Analysis With Javascript And Tensorflowjs
Chapter 1: What is Machine Learning (ML)? Basics of Java Script (JS) Programming in the browser using Java Script Graphics and Interactive processing in the browser using Java Script libraries Getting started with P5.JS and ML5.JS References
Chapter 2: Human Pose Estimation in the Browser Browser based data processing Posenet vs Openpose models Human pose estimation using ML5.Posenet Inputs, Outputs and Data...
Chapter 1: What is Machine Learning (ML)? Basics of Java Script (JS) Programming in the browser using Java Script Graphics and Interactive processing in the browser using Java Script libraries Getting started with P5.JS and ML5.JS References
Chapter 2: Human Pose Estimation in the Browser Browser based data processing Posenet vs Openpose models Human pose estimation using ML5.Posenet Inputs, Outputs and Data...
Beginning Machine Learning In The Browser Quickstart Guide...
Resumo
Beginning Machine Learning In The Browser Quickstart Guide To Gait Analysis With Javascript And Tensorflowjs
Nº de Páginas:
Encadernação: Capa Mole / Paperback
Tema: Artificial intelligence
Chapter 1: What is Machine Learning (ML)?
Basics of Java Script (JS)
Programming in the browser using Java Script
Graphics and Interactive processing in the browser using Java Script libraries
Getting started with P5.JS and ML5.JS
References
Chapter 2: Human Pose Estimation in the Browser
Browser based data processing
Posenet vs Openpose models
Human pose estimation using ML5.Posenet
Inputs, Outputs and Data structures of Posenet model
References
Chapter 3: Human Pose Classification
Classification techniques using ML Neural Network in the browser
Human Pose classification based on the outputs of Posenet model
Consideration of poses using Confidence scores of Posenet model
Storage of data using JSON formats related to the outputs of Posenet model
References
Chapter 4: Gait Analysis
Normal vs Abnormal Gait patterns
Determination of Gait patterns using threshold values of the models
User Interface design and development for monitoring of Gait patterns
Real-Time data visualization of the Gait patterns on the browser
References
Chapter 5: Future Possible Applications of Key Concepts
Nº de Páginas:
Encadernação: Capa Mole / Paperback
Tema: Artificial intelligence
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Características
- Editora
-
Apress
- Idiomas
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Inglês
- Peso
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308,0
- Data de lançamento
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17/03/2021
- Série/Edição Limitada
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1st ed.
- EAN
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9781484268421
Publicidade
Publicidade