Pose Estimation with TensorFlow.js

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This project is part of the liveProject series Human Pose Estimation with TensorFlow.js and React
prerequisites
basics of JavaScript • basics of HTML • basics of React • basics of TensorFlow.js • basics of machine learning and web development
skills learned
creating new modules within a React app • cleaning up the data • preparing data for training
Andrej Baranovskij
1 week · 4-6 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series Human Pose Estimation with TensorFlow.js and React liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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Look inside
In this liveProject you’ll use human pose data and TensorFlow.JS’s PoseNet to build and train a machine learning model that can recognize workout exercises. This model will record and recognize the workout session of a user, to be logged for future comparison. You’ll need to prepare data structures for the TensorFlow.js Dataset API, and execute one-hot-encoding with a simple mapping function. You’ll also define model architecture using the TensorFlow.js sequential API, and train your model using the fitDataset method. You’ll finish up by saving your trained model in local browser storage to allow for model reuse.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

you will learn

In this liveProject, you’ll learn how to prepare data, build a machine learning model, and save it for reuse. All of this will be done with JavaScript in the browser.

  • Creating new modules within a React app
  • Collecting and processing human pose data stream
  • Cleaning up the data, by removing pose data with low score
  • Preparing data for training
  • Constructing model architecture and building the model
  • Training model with TensorFlow.js fitDataset method
  • Saving the trained model for reuse
  • Displaying notifications to the app user

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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