Beginning machine learning in the browser: quick-start guide to gait analysis with JavaScript and TensorFlow.js
Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a sim...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Berkeley, CA
Apress
2021
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484268438/?ar |
| Zusammenfassung: | Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable programming language such as JavaScript to work with more approachable, fundamental coding ideas. Using JavaScript programming features along with standard libraries, you'll first learn to design and develop interactive graphics applications. Then move further into neural systems and human pose estimation strategies. For training and deploying your ML models in the browser, TensorFlow.js libraries will be emphasized. After conquering the fundamentals, you'll dig into the wilderness of ML. Employ the ML and Processing (P5) libraries for Human Gait analysis. Building up Gait recognition with themes, you'll come to understand a variety of ML implementation issues. For example, youll learn about the classification of normal and abnormal Gait patterns. With Beginning Machine Learning in the Browser, youll be on your way to becoming an experienced Machine Learning developer. You will: Work with ML models, calculations, and information gathering Implement TensorFlow.js libraries for ML models Perform Human Gait Analysis using ML techniques in the browser. |
| Beschreibung: | Step 2: Single-Person Pose Estimation Using a Browser Webcam. - Includes bibliographical references and index. - Print version record |
| Umfang: | 1 Online-Ressource (193 Seiten) |
| ISBN: | 9781484268438 1484268431 |
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| spelling | Suryadevara, Nagender Kumar VerfasserIn aut Beginning machine learning in the browser quick-start guide to gait analysis with JavaScript and TensorFlow.js Nagender Kumar Suryadevara Berkeley, CA Apress 2021 1 Online-Ressource (193 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Step 2: Single-Person Pose Estimation Using a Browser Webcam. - Includes bibliographical references and index. - Print version record Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable programming language such as JavaScript to work with more approachable, fundamental coding ideas. Using JavaScript programming features along with standard libraries, you'll first learn to design and develop interactive graphics applications. Then move further into neural systems and human pose estimation strategies. For training and deploying your ML models in the browser, TensorFlow.js libraries will be emphasized. After conquering the fundamentals, you'll dig into the wilderness of ML. Employ the ML and Processing (P5) libraries for Human Gait analysis. Building up Gait recognition with themes, you'll come to understand a variety of ML implementation issues. For example, youll learn about the classification of normal and abnormal Gait patterns. With Beginning Machine Learning in the Browser, youll be on your way to becoming an experienced Machine Learning developer. You will: Work with ML models, calculations, and information gathering Implement TensorFlow.js libraries for ML models Perform Human Gait Analysis using ML techniques in the browser. Machine learning Apprentissage automatique 9781484268421 Erscheint auch als Druck-Ausgabe 9781484268421 |
| spellingShingle | Suryadevara, Nagender Kumar Beginning machine learning in the browser quick-start guide to gait analysis with JavaScript and TensorFlow.js Machine learning Apprentissage automatique |
| title | Beginning machine learning in the browser quick-start guide to gait analysis with JavaScript and TensorFlow.js |
| title_auth | Beginning machine learning in the browser quick-start guide to gait analysis with JavaScript and TensorFlow.js |
| title_exact_search | Beginning machine learning in the browser quick-start guide to gait analysis with JavaScript and TensorFlow.js |
| title_full | Beginning machine learning in the browser quick-start guide to gait analysis with JavaScript and TensorFlow.js Nagender Kumar Suryadevara |
| title_fullStr | Beginning machine learning in the browser quick-start guide to gait analysis with JavaScript and TensorFlow.js Nagender Kumar Suryadevara |
| title_full_unstemmed | Beginning machine learning in the browser quick-start guide to gait analysis with JavaScript and TensorFlow.js Nagender Kumar Suryadevara |
| title_short | Beginning machine learning in the browser |
| title_sort | beginning machine learning in the browser quick start guide to gait analysis with javascript and tensorflow js |
| title_sub | quick-start guide to gait analysis with JavaScript and TensorFlow.js |
| topic | Machine learning Apprentissage automatique |
| topic_facet | Machine learning Apprentissage automatique |
| work_keys_str_mv | AT suryadevaranagenderkumar beginningmachinelearninginthebrowserquickstartguidetogaitanalysiswithjavascriptandtensorflowjs |