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

4.9
stars
36,627 ratings
4,758 reviews

About the Course

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization....

Top reviews

RK

Sep 02, 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 13, 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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4401 - 4425 of 4,709 Reviews for Convolutional Neural Networks

By taowucheng

Jan 05, 2018

最后一个作业感觉有一些问题

By A 6 W A

Sep 22, 2020

great course

By Steve d l C

Aug 28, 2020

good course

By Sahil M

May 05, 2020

The Besttt.

By Ankur G

Sep 15, 2019

informative

By SONIA D

Jan 31, 2019

Very Useful

By Luca B

Aug 01, 2018

Quite nice.

By Rishi J

Jul 25, 2020

Insightful

By Yehang H

Jan 02, 2018

Some error

By Mohamed A M

Oct 05, 2020

thank you

By Yashwanth M

Jul 16, 2019

Very Good

By Dave

Jul 10, 2020

good job

By PRASANNA V R

Jun 30, 2020

Decent

By CK P D

Mar 31, 2020

Thanks

By Niranjan

Nov 25, 2017

Great

By Sumera H

Sep 13, 2020

good

By Isha J

Apr 05, 2020

good

By Achutha s

Mar 27, 2020

good

By VIGNESHKUMAR R

Oct 24, 2019

good

By Rahila T

Oct 08, 2018

Good

By Naveen k

Jul 17, 2018

good

By Panchal S V

Jun 28, 2018

Good

By CARLOS G G

Jul 24, 2018

g

By Volodymyr M

Apr 24, 2020

This is not an education in any way. Yes, Convolutional Neural Networks provides good overview of convolutional networks and technology behind it. I like the way Andrew Ng structured material and his way to explain some details. Unfortunately, as a common problem for all "Deep Learning Specialization", theoretical material only scratches the surface of the knowledge. There is nothing deep in terms of theory. You will have to spend quite a lot of time digging for information yourself if you plan to use course material for any practical task, or assignment. In order to get missing pieces, I got to go through whole Spring 2017 CS231n. It is fine if you have enough time to see two sets of videos, but I expected to get same quality of material here, on Coursera.

Another course issue is quizzes. I am puzzled what these quizzes are testing. Provided answers often assume tentatively more than one correct variant. Probability theory works against you - you may happen to select correct answers for some questions , but definitely, not all of them. In the same time, it is quite easy to derive correct variant from second try.

Course programming assignments are complete disaster. While I kind liked programming assignments from week 1 and 2, I felt like I wasted my time working on programming assignments from week 3 and 4. I expected programming assignment to guide me through some training of complex networks, give some practical insight, which I can use for real-life tasks, but it was not there.

There is a good introduction to TensorFlow, while Keras is not even touched. And many assignments of week 3 and 4 are using Keras. It is necessary to peek-up theory and practice regarding Keras elsewhere. After one get enough knowledge about Keras elsewhere - guess what - programming assignment becomes useless as education, because it is too trivial.

I really wanted to rate this course as Two-Stars, but video materials and programming assignments from week 1 and week 2 slightly improved my attitude.

By Yair S

Sep 07, 2019

While the online teaching of Prof. Ng, is excellent as in the other courses, this course specifically, has several pitfalls which can not be ignored:

1) The teaching and cover being given for TensorFlow are by far insufficient. If this subject is seen as an essential part of the course, it must be instructed systematically but this is not the case, unfortunately. More often than not, you find yourself doing guesswork in the assignments when it comes to TF code, which is also reflected in the Discussion Forum. So to summarize, TF must be covered in a systematic way, either in this course, or a previous one.

2) There is a bug on week 4 NST assignment, on the given code. Should be fixed.

3) There are several written correction to errors in OnLine videos. These Videos can and should be rerecorded.

4) Last but certainly not least: I have experienced frequent and really disturbing connection problems with the Python Notebook, with frequent connection errors, which can not be recovered and wherein one must open again the Notebook. While this was, to some extent, the case in other courses, in this course it was much more of a problem, especially in Week 4, probably due to a large amount of data, and where each rerun requires another 20 - 30 minutes. a MUST fix.

Thanks,

Y. Shachar.