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学生对 DeepLearning.AI 提供的 Convolutional Neural Networks 的评价和反馈

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42,558 个评分

课程概述

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

热门审阅

SH

Aug 5, 2019

Great content in lectures! Automatic graders for programming assignments can be tricky, and set to old versions of tf sometimes, but answers to these issues are readily found in the discussion forums.

FH

Jan 11, 2019

Amazing! Feels like AI is getting tamed in my hands. Course lectures , assignments are excellent. To those who are not well versed with python - numpy and tensorflow , it would be better to brush up.

筛选依据：

2076 - Convolutional Neural Networks 的 2100 个评论（共 5,642 个）

创建者 Mudith W (

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Sep 3, 2020

Very good course with effective programming assignments

Thanks Andrew Ng.

创建者 yassine t

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Sep 1, 2020

a good course where we learn lot of things about computer vision and CNN

创建者 kameron b

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Jun 14, 2020

very comprehensive. A+ exposure to key concepts. feel ready to go deeper

创建者 Ahmed S

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Jun 12, 2020

High quality course with high quality valuable content. Thanks Andrew :)

创建者 Muhammad M M

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Jan 23, 2020

So beautiful explanation about Convolutional Neural Network Architecture

创建者 Wasiu S

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Jan 16, 2020

I think the programming assignments should be upgraded to Tensorflow 2.x

创建者 vishak b

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Nov 15, 2019

A Wide ranging course on CNNs and Computer Vision. Extremely Good stuff.

创建者 Sarut T

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Nov 12, 2019

very useful ituition but i feel like i still need to pratice more coding

创建者 Kartik S

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Jul 1, 2019

Excellent Course, though programming assignments can be made more tough.

创建者 Viona Q

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Jun 26, 2019

Very informative course. Loved the course materials and the assignments.

创建者 Onur G

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Mar 3, 2019

Great introduction to deep learning! I recommend this course to everyone

创建者 Akash G

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Feb 6, 2019

By Hard Way We Learn CNN..

Awesome Project And Experience..

I want Intern

创建者 莊雅婷

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Jan 23, 2019

The content is very useful. Prof. Ng's lecture is clear and interesting.

创建者 Qingyang X

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Oct 9, 2018

This course is really practical for the CNN beginner. Thank you, Andrew.

创建者 Rajaneesh T

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Jul 11, 2018

Very insightful - Adv CNN topics such as -Oneshot learning, ResNet , NST

创建者 Michael S

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Jun 24, 2018

Felt like I just took a world class course on a bleeding edge technology

创建者 Marvin P

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Jun 10, 2018

As good as a course about ConvNets can be. Thanks to Andrew and his team

创建者 Dipunj G

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Apr 26, 2018

Very Detailed course, should get you more than going in computer vision

创建者 Mikko L

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Mar 4, 2018

The examples were current, and relevant. This is a really useful course!

创建者 Fima R

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Dec 18, 2017

Good coverage, I personally would prefer more mathematical depth, though

创建者 Fanfan Y

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Dec 15, 2017

Great course! but there are couple of bugs in this course's assignments.

创建者 Tich M

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Dec 5, 2017

This one was adequately challenging for the level of material presented.

创建者 Rafael F

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Dec 1, 2017

Excellent course, very well laid out and the concepts are easy to grasp!

创建者 Ariful I M

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Nov 13, 2017

very nice course! Thank you Prof.

I wish to complete next course as well.

创建者 Muhammad T

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Mar 18, 2024

Course is good with balanced approach between theory and practical work