Heads up! This an end-to-end article series. In its three parts, I’m going to show you how to train, save, and deploy a recommender model. Specifically, you will understand how to get and process your data, build and train a neural network, package it in an application, and finally serve it over the internet for everyone to see and use.
At the end of this tutorial, you’ll have a book recommender application that can suggest books to users based on their history and preferences. We’ll get into the details of how this works shortly, but before that, below is the result of what you’ll be building:
Book Recommender Web Application
In this first part of the series, you will learn how to build and train the recommender model. In part 2, you’ll learn how to convert and embed the model in a web application, as well as make recommendations. And finally, in part 3, you’ll learn how to deploy your application using Firebase.
Table of Contents
- Introduction to recommender systems
- Downloading and pre-processing the book dataset
- Building the recommendation engine using TensorFlow / Keras
- Training and saving the model
- Visualizing the embedding layer with TensorFlow embedding projector
- Making recommendations for users
Build, Train and Deploy a Book Recommender System using Keras and TensorFlow.js, - Part 2
#keras #tensorflow
