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Traffic Sign Classification Using Deep Learning in Python/Keras
  • Level Intermediate
  • Duration 2 hours
  • Offered by

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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.

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Enhance your data science and AI skills with the "Traffic Sign Classification Using Deep Learning in Python/Keras" course on Coursera. This 1-hour project-based course, taught by an expert instructor, delves into Convolutional Neural Networks (CNNs) theory and application. Perfect for intermediate learners, it offers a practical approach to mastering traffic sign classification. Accessible for free, this concise yet comprehensive course is ideal for those looking to expand their deep learning knowledge efficiently.