Traffic Sign Classification Using Deep Learning in Python/Keras

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In this Guided Project, you will:

Understand the theory and intuition behind Convolutional Neural Networks (CNNs).

Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.

Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Deep LearningArtificial Intelligence (AI)Machine LearningPython ProgrammingComputer Vision

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Task 1: Project overview

  2. Task 2: Import libraries and datasets

  3. Task 3: Perform image visualization

  4. Task 4: Convert images to gray-scale and perform normalization

  5. Task 5: Understand the theory and intuition behind Convolutional Neural Networks

  6. Task 6: Build deep learning model

  7. Task 7: Compile and train deep learning model

  8. Task 8: Assess trained model performance

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Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Instructor

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