Traffic Sign Classification Using Deep Learning in Python/Keras

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En este proyecto guiado, tú:

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
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Deep LearningArtificial Intelligence (AI)Machine LearningPython ProgrammingComputer Vision

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Instructor

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