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Getting Started with TensorFlow.js

TensorFlow.js is a library for developing and training ML models in JavaScript, and deploying in browser or on Node.js For our purposes, TensorFlow.js will allow you to build Machine Learning models (especially Deep Neural Networks) that you can easily integrate with existing or new web apps.

Let’s start with ones(): You can use reshape() to change the dimensionality of a Tensor: You can use add() to do element-wise addition: and dot() to compute the dot product of two tensors: Finally, let’s have a look at transpose(): You can think of the transpose as a flipped-axis version of the input Tensor.

Here’s what our data looks like: Now, let’s render the data using barchart(): Note that we provide a DOM element to the renderer as a container for our chart, which might be handy when you want to embed the charts in your apps.

To make it somewhat realistic, we’ll try to approximate the conversion of kgs to lbs, which is described by this function: Let’s use it to prepare our data and create 2000 training examples: We’re going to use a style of Machine Learning known as Supervised Learning.

We’re going to go extremely simple: 1 layer, input size of 1, and 1 learning parameter: and teach it to convert kilograms to pounds: Your model needs a metric to know how well is doing.

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