Traffic Sign Classification Using Deep Learning in Python/Keras

4.6

359 個評分

提供方

10,421 人已註冊

在此指導項目中,您將:
2 hours
中級
無需下載
分屏視頻
英語(English)
僅限桌面

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.

您要培養的技能

  • Deep Learning

  • Artificial Intelligence (AI)

  • Machine Learning

  • Python Programming

  • Computer Vision

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

授課教師

審閱

來自TRAFFIC SIGN CLASSIFICATION USING DEEP LEARNING IN PYTHON/KERAS的熱門評論

查看所有評論

常見問題