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返回到 TensorFlow for AI: Applying Image Convolution

学生对 Coursera Project Network 提供的 TensorFlow for AI: Applying Image Convolution 的评价和反馈

14 个评分


This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 1.5-hour long project-based course, you will discover convolutions, apply filters to images, apply pooling layers, and try out the convolution and pooling techniques on real images to learn about how convolutions work. At the end of the project, you will get a bonus deep learning project implemented with Tensorflow. By the end of this project, you will have learned how convolutions work and how to create convolutional layers to prepare for your own deep learning projects using convolutional neural networks. This class is for learners who want to use Python for building convolutional neural networks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a knowledge-based course about convolutions in images with TensorFlow. Also, this project provides learners with needed knowledge about building convolutional neural networks and improves their skills in applying filters to images which helps them in fulfilling their career goals by adding this project to their portfolios....



1 - TensorFlow for AI: Applying Image Convolution 的 5 个评论(共 5 个)

创建者 Diego F B H

Oct 27, 2020

The way to explain convolutional operations is right and well demonstrated with visual results that are pretty comprehensible for any one person.

创建者 Ed H C

Dec 7, 2020

I love this training but I need to review again and again I think to understand it fully.

创建者 Rohit P

Dec 30, 2020

Thank you

创建者 Christos G

Dec 9, 2020

A very nice explanation of convolutional and pooling operations accompanied by visual results that are comprehensible and easily understandable.

创建者 Robert L P

Nov 23, 2022

Needs more hands-on.