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Self-Driving Car Engineer Nanodegree

Deep Learning

Traffic Sign Recognition Program

Overview

This model uses deep neural networks and convolutional neural networks to classify traffic signs. The model will be trained on traffic signs from the German Traffic Sign Dataset. A training set, validation set and testing set were used. The testing set was keeped seperate and untouched until after the model was completed. The model had an accuracy of 94% on the testing set.

How to view

The report can be viewed by clicking the notebook Traffic_Signs_Recognition.ipynb above.

Also, later after I learnd Keras I rewrote the model with Keras code. This model can be viewed by clicking traffic-sign-classification-with-keras.ipynb above. The test accuracy increased to 95.9%