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.
When you start your liveProject, you get full access to the following books for 90 days.
- Basics of HTML
- Basics of Node.js, NPM, and Yarn
- Basics of React
- Basics of TensorFlow.js
- Visual Studio Code
- Basics of machine learning
you will learn
- 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
- 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.