Traffic Sign Classification Using Deep Learning in Python/Keras

About this guided project

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.

What skills will you gain?

Artificial Neural Networks

Artificial Intelligence and Machine Learning (AI/ML)

Keras (Neural Network Library)

Applied Machine Learning

Tensorflow

Machine Learning

Deep Learning

Computer Vision

Artificial Intelligence