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Learner Reviews & Feedback for Neural Networks and Deep Learning by

58,414 ratings
11,096 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


Jul 15, 2019

Dear Andrew! Thank you so very much for making me belive in myself as a machine learning engineer. Your lectures & excercises are like "shoulders of Giants" on which a good student can stand out high.


Apr 07, 2019

A bit easy (python wise) but maybe that's just a reflection of personal experience / practice. The contest is easy to digest (week to week) and the intuitions are well thought of in their explanation.

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10476 - 10500 of 10,891 Reviews for Neural Networks and Deep Learning

By Juan D S M

Aug 22, 2017

This is an excellent course, very easy to follow! The assignments are very simple though. I missed some

By Petr H

Jun 07, 2018

Course is great, but I think the exercises have too much handholding. I like to struggle with problems a bit so that I need to understand it from different angles before I find a solution. In this course all the exercise were a bit too straight forward.

By Jacek S J

Feb 17, 2018

Very good course and intro to deep learning. I love the jupyter notebooks integration. I was able to complete the assignments from my iPhone using Safari. The assignments were a little too easy. I didn’t feel the usual “pain” of learning.

By Clayton G

Nov 04, 2017

The programming exercises involved a little too much hand-holding.

By Andreas

Jan 23, 2018

reasoning for why neural networks work comes from previous ML-course.

In this one, it's more about hands-on programming and getting familiar with python and numpy.

The exercises themselves are pretty easy to solve.

If one were to implement them on his own it would be 10x harder, but one can build on them.

Thanks again to everyone for preparing this course

By sada n

Jul 02, 2018

Few glitches in video and assignments were solid, rarely confusing; overall a great class

By John H

Aug 17, 2017

Week 2 was very difficult but work really hard at it and things get better.

By Daniel R F C

Sep 16, 2017

It's a good course, I learned so much, I guess it could be better if it encourages and give good practices to the students to build an entire learning algorithm in their own machines, I mean, Jupyter Notebook is good but in the future we going to need to apply this algoritms by ourselves in different environments and in the notebooks there are so many things that are already done so I guess it could be trickly when we try to do all by ourselves.

By Dániel S

Apr 17, 2018

Quality curriculum, maybe just a little repetitive.

By aaron j

Aug 15, 2017

Andrew Ng really takes all the stress out of the complexities involved in grasping neural networks. His relaxed voice and clear explanations have really clarified some of the murkier points. "don't worry about it". He's the best.

By Aditya G

Jun 22, 2018

There seemed to be slight inconsistencies in some slides, but overall the course was good! Already having knowledge of neural network, it wasn't too hard to understand or follow.

By Shuai X

Dec 15, 2017

This course offers similar contents as the part of the Stanford Machine Learning Course on neural networks. One useful addition is practices on implementing deeper neural networks with more than one hidden layers using Python. People with basic knowledge of linear algebra can complete this course in a day (i.e. 10 hours) by skipping less important videos.

By Serena P

Apr 29, 2018

Thank you for your teaching, Andrew Ng!

By Eirik P

Nov 05, 2017

A good introduction, though if you understand matrix algebra and calculus then it's a bit slow - I feel we have a lecture which simply restates what is fairly obvious from prior knowledge.

By Shanshan H

Jul 07, 2018

the lectures are very clear though kind of verbose, but the assignments are far from challenging.

By 苑思域

Jul 09, 2018


By Santosh S

Mar 03, 2018

Great course to learn more about Neural Networks basics

By Shrey P

May 29, 2018

Good introduction to deep learning. Great for developing an intuition on the mathematics behind NN. However, programming assignments are too easy. Most of the things are already done for you. I am expecting more complexity in the later parts of this specialization.

By Sarvesh G

Oct 14, 2017

Assignments should be little more difficult.

By Bhaskar C

Mar 03, 2018

This course structure is indeed very good. It would have been great if the deepnet libraries like TensonFlow are introduced very briefly.

By Deepjyoti D

Feb 04, 2018

thought there will be more math in this, probably the things I thought would be more covered will be in the other courses. Andrew is of course a phenomenal teacher.

By 王文军

May 16, 2018

good course.But some course content was just the same with Machine Learning Course

By Alexander

Sep 25, 2017

Good chewing basics. In this Mr. Andreev Ng is very good.

Homework is very easy

By Alekhya G

May 31, 2018

the assignments could have been a little more challenging

By priyank y

Feb 11, 2018

Good course for beginners and teaches each and every concept step wise. A small suggestion to modify the assignments in order to make it more challenging for students