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返回到 Traffic Sign Classification Using Deep Learning in Python/Keras

学生对 Coursera Project Network 提供的 Traffic Sign Classification Using Deep Learning in Python/Keras 的评价和反馈

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359 个评分

课程概述

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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NB

Jun 20, 2020

Very nice course, everything was explained perfectly.

Can also add about testing the trained model using external data, like if we want to give an input and perform prediction then how it is done.

FB

May 21, 2020

Instructor was efficient in delivering the knowledge and I understood it very well. The exercises were also great. Overall, my aim for taking this course had been accomplished.

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1 - Traffic Sign Classification Using Deep Learning in Python/Keras 的 25 个评论(共 52 个)

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