Convolutional Neural Networks
DeepLearning.AI · View on Coursera ↗
Taught by Andrew Ng, Kian Katanforoosh, Younes Bensouda Mourri
Data ScienceComputer Science
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 the
The dissent
The most-upvoted critical review (53 votes, 3.0★).
“This is most frustrating course in all of your courses so far. The instructions were completely misguiding the candidates from YOLO implementation onwards. All along you presented the course very well. But when come to most important topics, we had to focus on syntactical errors. But we are supposed to spend time on understanding the algorithms at this level. Dont know why this 180 degrees turn taken by you. If you intentionally designed this course then fine. Otherwise, you should seriously think about rework on the instructions. Few links to hints were taking to some pages in github with just folders.”
Coursera's 4.9 comes from 42,614 ratings. Of those, 0.5% are 1–2★.
Starisk reads the reviews learners voted most helpful, and weights each by the square root of its votes: a review with 80 upvotes counts about five times one with 2. Unvoted reviews are not skipped out of laziness — by this model they carry no evidence that other students endorsed them.
That measures what the most-endorsed reviews say, not “true quality.” The rating distribution above is the whole population, so you can see the sample next to it.
Reviews read on 22 Oct 2026.Read them on Coursera ↗