Traffic Sign Classification Using Deep Learning in Python/Keras

Overview

Class Central Tips

In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Convolutional Neural Networks (CNNs). - Import Key libraries, dataset and visualize images. - Perform image normalization and convert from color-scaled to gray-scaled images. - Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. - Compile and fit Deep Learning model to training data. - Assess the performance of trained CNN and ensure its generalization using various KPIs. - Improve network performance using regularization techniques such as dropout.

Syllabus

Classify Traffic Signs Using Deep Learning for Self-Driving Cars

In this hands-on project, we will train deep learning models known as Convolutional Neural Networks (CNNs) to classify 43 traffic sign images. This project could be practically applied to self-driving cars. In this hands-on project we will go through the following tasks: (1) Import libraries and datasets (2) Images visualization (3) Convert images to gray-scale and perform normalization (4) Build deep learning model (5) Compile and train deep learning model (6) Assess trained model performance