Chevron Left
Back to Neural Networks and Deep Learning

Learner Reviews & Feedback for Neural Networks and Deep Learning by deeplearning.ai

4.9
61,985 ratings
11,722 reviews

About the Course

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization....

Top reviews

MH

Jun 30, 2018

Very good course to start Deep learning. But you need to have the basic idea first. I would suggest to do the Stanford Andrew Ng Machine Learning course first and then take this specialization courses

JP

Feb 12, 2018

I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!

Filter by:

51 - 75 of 11,530 Reviews for Neural Networks and Deep Learning

By Brandon C

Dec 04, 2018

Extremely helpful review of the basics, rooted in mathematics, but not overly cumbersome. Very clear, and example coding exercises greatly improved my understanding of the importance of vectorization.

By Benito C

Sep 02, 2017

Very hard work in designing the notebooks so the pupils's learning processing is maximized.

By William M

Sep 04, 2017

I really enjoyed taking this course. I have taken one of Andrew's courses before, and they keep getting better. I have a background in development, and appreciated the use of python over octave. Andrew consistently strives to provide an intuitive feel for the topics he is presenting. The fact that he is able to provide a complex subject in a simple manner speaks to his mastery of the subject.

The course contained a great mix of theory and practical application of those theories. I'm looking forward to the next course.

By Kieran S

Oct 22, 2017

Extremely well structured course that gives you good intuition about how deep learning works by starting with simply examples and adding layers of complexity.

By 华德禹

Aug 23, 2017

greate

By Abdessalem H

Dec 03, 2017

This is one of the courses I enjoyed the most. For someone who has little to no knowledge in calculus and programming, I found the course is well tailored for all kinds of background. The pace is not so fast and Andrew is making it so easy even for beginners to grasp the new jargon and formulae. Thank you Coursera. Thank you Andrew.

By Anjan D

Oct 01, 2017

Excellent course with great assignments. I have learnt from the beginner level in DL. It also helps one to brush up the calculus and linear algebra knowledge very much.

By SAGAR B

Sep 10, 2017

A great course to understand basic concepts of Deep Learning. If you are a beginner in Deep Learning and thinking if you should invest your time and money here, don't give a second thought and join right away. Andrew Ng never disappoints!

By fahad

Aug 25, 2019

This course was really clear my concepts of Deep Learning and how actually neural network works.

By David R

Oct 01, 2019

(09/2019)

Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

By Alessandro

Sep 09, 2017

The content is great and I learned a lot. Certainly there could be a lot more feedback by the instructor in the forum. My feeling is that the students are really left on their own. Good from one point of view (cause you really have no choice than crush your head on the problem for days until you understand or give up), bad from another (it takes a lot longer to clarify difficult points). Fortunately the forum is populated by very clever students that take the time to answer questions. As a beginner I learned the broad strokes and intuitions for NN in this course, but the details about certain formulas are still very obscure and I was hoping for a better explanation of those.

By Omar A

Jul 22, 2019

If you have taken this course after ML by Andrew, you will see exactly the same material covered in 1 week expanded in 4 Weeks except using Python instead of octave or Matlab.

If you have calculus background I expect you to get tedious from elementary approaches in the lectures to get rid of Math and calculus.

Programming exercises in this course are very easy and below the level of first excellent experience with ML course.

There is no easy way to get lectures slides, No reading sections in this course. Like this course made to make systematic approaches to get things done without actual care about understanding the theories and concepts.

The good news comes when you have no previous knowledge about NN and elementary python skills, then this course is an excellent way for you to start.

By Francis J

Dec 29, 2017

too easy, suitable as an entry level class

By Muhammad A

Aug 20, 2018

Great attempt but it failed to provide complete details. Specifically the project files and their loading mechanism

By Ofer B

May 01, 2018

Very abstract, and the examples are not as concrete as they could be. I'd use better visuals to ensure that the concepts in each video are understood 100% visually.

By Aratz S

Feb 27, 2018

Easy course if you have coursed the ML course before. I would like to see more explanations in detail. Still some bugs in the assignments... why???

By Tobias G

Feb 21, 2018

Few Detail. Mathematics missing.

By Jérôme B

Nov 16, 2017

To me, this is a failed attempt at simplifying those concepts. After spending hours trying to figure it out, now I find the algorithm behind the Neural Network very simple, and I can easily explain it to someone. But in this course I had to figure out by myself what was the point of those hundreds of lines of maths. So, very interesting concepts, but the "transmitting style" wasn't for me.

By Andrew H

Apr 28, 2019

Not enough explanation or support to complete the very vaguely worded assignments in anything like the specified timescales.

I respect the source of this course but as a teaching resource it is really very poor.

By Zaheer

Apr 10, 2019

This course is really good but assignment given to solve is not understandable.

By Joseph K

May 20, 2018

It will be a good course when you dump jupyter note books.

By Felix F

Dec 20, 2017

giving low grade for ongoing delays of course 5

By Long H N

Dec 10, 2017

N/A

By Maxence A

Oct 29, 2017

The programmation exercice are nice, but the courses are mainly about very basic linear algebra.