Beginning Machine Learning in the Browser
- Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js
- Indbinding:
- Paperback
- Sideantal:
- 182
- Udgivet:
- 2. april 2021
- Udgave:
- 1
- Størrelse:
- 155x235x0 mm.
- Vægt:
- 454 g.
- 8-11 hverdage.
- 30. november 2024
Normalpris
Abonnementspris
- Rabat på køb af fysiske bøger
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Beginning Machine Learning in the Browser
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 MLimplementation issues. For example, yoüll learn about the classification of normal and abnormal Gait patterns.
With Beginning Machine Learning in the Browser, yoüll be on your way to becoming an experienced Machine Learning developer.
What Yoüll Learn
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
Who This Book Is For
Computer science students and research scholars, and novice programmers/web developers in the domain of Internet Technologies
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 MLimplementation issues. For example, yoüll learn about the classification of normal and abnormal Gait patterns.
With Beginning Machine Learning in the Browser, yoüll be on your way to becoming an experienced Machine Learning developer.
What Yoüll Learn
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
Who This Book Is For
Computer science students and research scholars, and novice programmers/web developers in the domain of Internet Technologies
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