Comparing Model Performance with Normalised vs standardised input (Traffic Sign Classifier)

Jessica YungData Science, Self-Driving Car ND, StatisticsLeave a Comment

In the previous post, we explained (1) what normalisation and standardisation of data were, (2) why you might want to do it and (3) how you can do it. In this post, we’ll compare the performance of one model on unprocessed, normalised and standardised data. We’d expect using normalised or standardised input to give us higher accuracy, but how much better … Read More

Traffic Sign Classifier: Normalising Data

Jessica YungSelf-Driving Car ND, Statistics1 Comment

In this post, we’ll talk about (1) what normalising data is, (2) why you might want to do it, and (3) how you can do it (with examples). Background: The Mystery of the Horrifically Inaccurate Model Let me tell you a story. Once upon a time, I trained a few models to classify traffic signs for Udacity’s Self-Driving Car Nanodegree. I first … Read More

Bugger! Detecting Lane Lines

Jessica YungArtificial Intelligence, Highlights, Self-Driving Car ND3 Comments

This week I’ve been working on the first project of Udacity’s Self Driving Car Engineer Nanodegree – annotating lane lines in images and videos recorded from a front-facing camera mounted on the front of a car. There are plenty of blog posts floating around explaining the concepts (here’s my project code with a walkthrough of the process of annotating images), … Read More