Coding a deep learning model using TensorFlow.js

In the previous tutorial "An introduction to AI in Node.js", we explained two basic approaches for embedding a deep learning model in your Node.js application. In this tutorial, we go a step further and show you how to build and train a simple deep learning model from scratch. Therefore, unlike the previous tutorial, you need a more in-depth understanding of how deep learning models work to get the most benefit from this tutorial. We start with the programming concepts for deep learning and cover two different programming APIs: the high-level Layers API and the low-level Core API. You'll code a simple model to classify clothing items, train it with a small data set, and evaluate the model's accuracy. Then, to illustrate a common practice in deep learning, you'll take your trained model and apply transfer learning to teach the model to classify new items. We also describe how to take a pre-trained model from other sources such as Python and convert it to a format that can be used in JavaScript. So far, we have seen that the actual deep learning model can be hidden in an npm package, loaded from a binary format, or served through a REST API. In these cases, we are simply running an inference on the model, and we don't care how the model was implemented.