Neural Networks and Deep Learning
DeepLearning.AI · View on Coursera ↗
Taught by Andrew Ng, Kian Katanforoosh, Younes Bensouda Mourri
Data ScienceComputer Science
About the course
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specializati
The dissent
The most-upvoted critical review (287 votes, 3.0★).
“I enjoyed the lectures and a few practice quiz. But I don't think the structure of assignments presented here is the correct way to assess learning. The assignments or exercises should be interspersed between lectures and the problems should be more interactive (pushing the student to think). Moreover, the amount of pre-written code was immense and therefore didn't really make me think a lot on my own. This structure of assignment forces the student to focus on matching the expected output instead of really understanding the concept. I am pretty sure most students did not really grasp the concepts at an intellectual level but still passed with decent grades. This is exactly the problem with schools today and I hope that Coursera is working towards rectifying that.”
Coursera's 4.9 comes from 123,839 ratings. Of those, 0.3% 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 ↗