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prerequisites
basics of JavaScript • basics of HTML • basics of React • basics of TensorFlow.js • basics of machine learning and web development
skills learned
setting up TensorFlow.js PoseNet model in React Web app • enabling a WebGL backend • setting up Webcam live stream processing
Andrej Baranovskij
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll set up and tune a TensorFlow.js PoseNet model in a React web app. PoseNet allows real-time human pose estimation in your browser, all through the magic of TensorFlow.js. You’ll enable a WebGL backend, set up a system where PoseNet can utilize a webcam stream for its data input, and establish a canvas that can draw on top of an estimated pose.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Andrej Baranovskij
Andrej Baranovskij is a TensorFlow-certified developer who runs his own machine learning startup company Katana ML. Andrej is responsible for building machine learning products for enterprise operations automation. Previously, he spent 15 years working with Oracle technology and building various enterprise systems across the globe. His software development experience allows to bridge a gap between machine learning and software development.

prerequisites

This liveProject is for both web developers and machine learning engineers. Web developers experienced with JavaScript will learn how to integrate TensorFlow into their applications, while ML engineers will be interested in developing their skills for user interfaces.

TOOLS
  • Basics of JavaScript
  • Basics of HTML
  • Basics of Node.js, NPM, and Yarn
  • Basics of React
  • Basics of TensorFlow.js
  • Visual Studio Code
TECHNIQUES
  • Basics of machine learning
  • Basics of web development with HTML/JavaScript

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