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Learner Reviews & Feedback for Convolutional Neural Networks by DeepLearning.AI

4.9
stars
42,355 ratings

About the Course

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....

Top reviews

RK

Sep 1, 2019

This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.

AG

Jan 12, 2019

Great course for kickoff into the world of CNN's. Gives a nice overview of existing architectures and certain applications of CNN's as well as giving some solid background in how they work internally.

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2076 - 2100 of 5,619 Reviews for Convolutional Neural Networks

By Kartik S

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Jul 2, 2019

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

By Viona Q

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Jun 27, 2019

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

By Onur G

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Mar 3, 2019

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

By Akash G

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Feb 6, 2019

By Hard Way We Learn CNN..

Awesome Project And Experience..

I want Intern

By 莊雅婷

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Jan 24, 2019

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

By Qingyang X

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Oct 9, 2018

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

By Rajaneesh T

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Jul 11, 2018

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

By Michael S

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Jun 24, 2018

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

By Marvin P

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Jun 10, 2018

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

By Dipunj G

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Apr 26, 2018

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

By Mikko L

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Mar 4, 2018

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

By Fima R

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Dec 18, 2017

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

By Fanfan Y

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Dec 16, 2017

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

By Tich M

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Dec 5, 2017

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

By Rafael F

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Dec 1, 2017

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

By Ariful I M

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Nov 13, 2017

very nice course! Thank you Prof.

I wish to complete next course as well.

By Muhammad T

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Mar 18, 2024

Course is good with balanced approach between theory and practical work

By sarathva v

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Jan 26, 2024

Course is really good but need some explanation to tensor flow coding .

By Morteza M

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Sep 24, 2022

perfeeeeeect.just if use pytorch rather than tensorflow is much better

By Levin

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Aug 8, 2022

Amazing course!

I get big intuition about Convolutional Neural Networks.

By vinoth k

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Dec 26, 2021

Best ever course for those who are in need of strong foundation in CNN.

By Pasan J

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Oct 4, 2021

Amazing course! Very detailed and explained as expected! Thanks andrew!

By Rugved R K

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Aug 4, 2021

Great course, Learning a lot of things, very good for beginners like me

By sushil d

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Jul 31, 2021

very helpful to understand different vision models with hands on coding

By Salma H S

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Oct 11, 2020

I wish the illustration of TensorFlow and Kares to go deeper than this.