- TensorFlow.js is an open source library that lets you build and train ML models in your browser
- It’s available with GPU acceleration and also automaticlaly supports WebGL
- You can import existing pre-trained models and also re-train entire existing ML models in the browser itself
You might be wondering at this point what’s the advantage here of using a browser to build ML models.
You can also open the webpage on your mobile or tablet which will enable your model to take advantage of sensor data.
You can import an existing pre-trained model and TensorFlow.js converters will make it browser ready for you. You can also re-train existing ML models using “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”. You can re-train the model very quickly and efficiently with this while only requiring a small amount of data.
How do I install TensorFlow.js?
It’s pretty straightforward. You can use it by installing in from NPM:
yarn add @tensorflow/tfjs
npm install @tensorflow/tfjs
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]"></script>
Check out the TensorFlow.js website and their GitHub page to read more about this release.
Our take on this
TensorFlow.js is basically the successor of deeplearn.js. The major difference between the two is the TensorFlow.js includes a layers API and imports pre-trained models and can also re-train them. Also, you can work on almost any GPU but it will not be close to the speed you’ll get on CUDA.
Are you planning to use this for building your models? What do you think about this latest TensorFlow release? Let us know in the comments section below!
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