Introduction

TensorFlow.js, an open-source library you can use to define, train, and run machine learning models entirely in the browser, using Javascript and a high-level layers API.

ML running in the browser means:

• No need to install any libraries or drivers. Just open a webpage, and your program is ready to run.

• Ready to run with GPU acceleration. TensorFlow.js automatically supports WebGL, and will accelerate your code behind the scenes when a GPU is available.

• Mobile device support, in which case your model can take advantage of sensor data, say from a gyroscope or accelerometer.

• All data stays on the client, making TensorFlow.js useful for low-latency inference, as well as for privacy preserving applications.

Some Considerations:

• You can import an existing, pre-trained model for inference.

  If you have an existing TensorFlow or Keras model you’ve previously trained offline, you can convert into TensorFlow.js format, and load it into the browser for inference.

• You can re-train an imported model.

  You can use transfer learning to augment an existing model trained offline using a small amount of data collected in the browser using a technique called Image Retraining.

• Author models directly in browser.

  You can use TensorFlow.js to define, train, and run models entirely in the browser using Javascript and a high-level layers API.