Learning and growing, currently in data science
Welcome to the third and last part of the bread journey! In parts I and II, I wrote about how I scraped data from The Fresh Loaf to get recipes, explored and visualized the text data and used topic modeling to see what trends exist. In this post, I will describe how I trained two different language generation models to predict AI-based recipes for sourdough bread.
Welcome back! I previously described how I scraped the baking forum The Fresh Loaf, where people post their bread recipes, to get data to train a neural network to generate new bread recipes. I also detailed how I explored the data. In this post, I explain how I used some unsupervised learning techniques in the Natural Language Processing toolkit to further understand the textual data. Note: all the code I used for this project is in this repo.
In 2017, a friend gave me some sourdough starter to make bread with, and ever since then, my life has changed. It sounds cheesy, but I discovered a hobby that has led me to buy almost 200 pounds of flour at a time (seriously), develop a biweekly pizza baking habit, and dream of what bread I’m going to make in the coming days!
Because I spend a lot of time baking sourdough and experimenting with new formulas, I wanted to see if I could create an artificial intelligence-powered recipe generator that would predict something for me to make! One of my go-to websites for technique, tips and tricks has been the helpful bread baking forum, The Fresh Loaf, where people ask questions and post recipes. My idea was to scrape this website and get data to train a neural network to generate new bread recipes – and that’s what I did. At the end of this project, I was able to achieve my goal: to bake a machine learning-inspired loaf of bread, with ingredients predicted with a neural network.
Since there are multiple components to this project, I am breaking them down in a few blog posts. All the code I used for the project is in this repo.
