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

4.9
stars
40,149 ratings
5,315 reviews

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

RS
Dec 11, 2019

Great Course Overall\n\nOne thing is that some videos are not edited properly so Andrew repeats the same thing, again and again, other than that great and simple explanation of such complicated tasks.

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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4751 - 4775 of 5,288 Reviews for Convolutional Neural Networks

By Narayanan S

Mar 15, 2019

Very good introduction. I wish the assignments were a bit more challenging

By Alex N

Dec 27, 2017

Please fix the grader in Week 4 - Face recognition - Triplet loss exercise

By Pierrick R

Feb 27, 2021

I think that the exercices are really easy, you have to change this part.

By Ahouba C A A

Jun 24, 2020

I wish there had been more explanations something regarding propagations.

By Kalyan A

May 2, 2020

one less star because you are not giving me any material for this course.

By MEKALA S N

May 1, 2020

Overall course was good. Some videos are lengthy, people might get bored.

By Christian A

Feb 26, 2019

excellent course. the jupyter notebooks were behaving erratically though.

By Amit K

Apr 14, 2018

this one was hard to clear thanks for wonder full tutorials and questions

By Fereydoon V

Feb 26, 2018

Tensorflow and Keras tutorials need improvement and further explanations.

By Benjamin H D

Dec 11, 2017

Material is great as always, the audio could certainly be improved though

By Katharina E

Jan 9, 2021

Content is great, but the auto grader has issues costing a lot of time!

By Michael F

May 24, 2018

Programming assignments are too easy, consisting largely of copy&paste.

By Richard Y

Feb 25, 2018

Very good course. Just please fix the buggggggggy grader in the week 3.

By Pengbo L

Jan 24, 2018

The last assignment on triple-loss has the grader-error, which a couple

By Dino P

Jul 2, 2020

I'd have given it 5 if programming exercises were modified to use TF2.

By Ukachi O

May 10, 2020

A wonderful introduction and implementation to the concepts of CovNets

By Vamvakaris M

Sep 8, 2019

It required coding on keras and tensorflow not appropriete introduced.

By Emmanuel R

Jun 10, 2018

Very hard at week 2. Week 3 and Week 4 were very exciting. I liked it.

By Clay R

Feb 21, 2018

The grader could use some more debugging but otherwise excellent work.

By David P

Dec 6, 2017

Great course! Assignment notebooks could be a bit more challenging...

By Rafael G M

Jan 26, 2020

Good material.

I recommend explaining YOLO with more conceptual depth

By Jaisuthan A

Sep 2, 2020

Nice course. But we are not working on latest versions of Tensorflow

By Jinfeng X

Nov 12, 2019

Great content! It would be better if some missing slides were there.

By 龚华君

Jul 23, 2019

Neural style transer part is hard to understand, the rest part easy

By Lin Z

May 7, 2019

interesting introduction materials on convolutional neural networks.