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Everywhere we go online nowadays seems to be followed by something like “your top picks” or “based on your previous…”; these Netflix movies that appear more often, or Spotify songs that keep showing up on your song radio are a result of recommender systems. These systems can sometimes be annoying and a little intrusive but, to their credit, they do save us sifting through the mountain of content by giving us a realistic shortlist to get through.

The recommender system I created is limited compared to that of Netflix that has far more data; like user age, which can have…


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Introduction

I want to use this post to highlight how easy it was to deploy an accurate deep learning web application. After starting the FastAI course recently, I’ve learned the basics of image processing and managed to build an app with one of the models. FastAI recommends using Binder, but I found this to be very slow compared to Streamlit.

I trained the model in a Colab notebook where I can use a GPU; then exported it to Streamlit. All the code is available in this repository, complete with example notebooks. The Streamlit dashboard is here.

Bing API

Automating the data collection process…


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According to IMDb, it has over half a million movie titles and almost 5.5 million user reviews, with each of these reviews is a 1–10 rating and text review; I want to use these reviews to train a model that can guess future reviews’ sentiment. The type of natural language processing is called text classification. Much of the work here is from Lesson 8 of the FastAI course which, I cannot recommend enough to anyone interested in deep learning. Follow along with the notebook used to train the model.

Preprocessing

Text is a pretty abstract concept to a computer that only…

Jack Harding

Data Scientist | BSc Computer Engineering | jacklinc.github.io

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