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Learner Reviews & Feedback for Neural Networks and Deep Learning by DeepLearning.AI

4.9
stars
104,758 ratings
20,941 reviews

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 Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

OO
Oct 20, 2017

Andrew Ng's presenting style is excellent. Makes the course easy to follow as it gradually moves from the basics to more advanced topics, building gradually. Very good starter course on deep learning.

AA
Sep 1, 2019

I highly appreciated the interviews at the end of some weeks. I am currently trying to transition from a research background in Systems/Computational Biology to work professionally in deep learning :)

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351 - 375 of 10,000 Reviews for Neural Networks and Deep Learning

By barryhf

Mar 27, 2019

It's an honor to be taught by Professor Ng. He's an excellent instructor, and he has very effectively brought this complex material to those of us who are practitioners rather than applied mathematicians.

Perhaps if, like me, you are familiar with the mathematics used in this work, you might find the pace a bit slow. The repetition, and the guided programming exercises, do serve a valuable purpose. By the end, by the final exercise, there is crisp clarity on what all of the components of the neural network are, and how they are utilized.

Thank you, professor, for an excellent course.

By Stanislava F

Aug 31, 2020

Good balance between theory and practice. The best thing is that everything that you learn during the course you also try in the notebook, right away. So all the formulas and computations become very clear. I have been working with NNs for a couple of years by now and took this course to refresh the knowledge - and surprisingly I have found a couple of things I didn't know before (or have successfully forgotten about). I would also thank Andrew Ng for taking the interviews and sharing these inspiring videos with the students, it's very motivating hearing their stories and advice.

By Mohammad S Q

Feb 19, 2019

First of all the course is designed and taught by AI pioneer Andrew Ng, the fact itself creates no room for any reason for not opting for this course if somebody wants to learn about DL.

Secondly, the approach is ground up, you get a confidence that without knowing or learning complex numerical foundations, you can get intuition of how deep learning works and can very well start applying this into your projects. When you see working model of a deep neural network built from fundamental codes, you feel like doing something and it makes you try harder and wider problems on your own.

By Naima

Sep 27, 2017

The course is very helpful. Andrew Ng has explained all the basics of neural networks. Both theory and programming lessons are very neatly arranged. It helps freshers to learn a lot. Since in programming assignment, the theory and notations needed for that are also explained I could connect everything fast. And I didn't had to code everything in python. It helps people who are not that much expert in python and its an inspiration to learn more in python and other technologies. I express my gratitude towards Coursera and Mr.Andrew Ng for helping for this course. Thanks, Naima Vahab

By Christen

Sep 9, 2017

I had almost zero knowledge about Python language and even less about all the complexity of the internal structure of neural networks (NN). I can imagine how difficult is teaching this sort of witchery to common mortals but Andrew Ng. did a great job on that simplifying and remarking just the practical and important points you require to build a simple NN. It's a clever way to start in the world of deep learning despite of the high price of this course, otherwise it could take ages learning by yourself. I wonder if I will become a kind of wizard when I finish all the 5 courses...

By Rahul D

Sep 6, 2020

I think this course was designed as what I need. I always want to learn from the teacher who taught me the basics and the development of the core part of the technology rather than touching its outer part and showing me where it can apply. This course tatught me how actually a neural network is built in the system and how it actually works. This course helped me lot to understand the nural networks. Everywhere on the internet what I found is just application of neural networks, no one focused on teaching the basic way designing neural networks. Thank you for this amazing course.

By Sabarish V

Dec 3, 2018

The course takes a very direct approach to building your first neural network. It has very little maths, and the coding is extremely simplified in the assignments. For someone with a little bit of background, it wouldn't take more than a couple hours to be done with the course and running your first multi-layer model. If you have prior knowledge of NNs, machine learning, or calculus and vectorization, the course could feel a bit tedious. In this case, I'd recommend running the videos at 1.5x or 2x speed .

My only gripe is the quality of the audio. It could have been much better.

By Eche I

Oct 12, 2018

I was initially running from the maths that underpins deep learning but this course made it some much easy to understand and gain intuition on how to operate deep learning. I thoroughly enjoyed Andrew's style of delivery and with his constant reassurance and I quote, "don't worry about it …", that holds very true and gradually makes you fall and gets the underlying linear algebra, calculus and derivatives that form the theoretical backbone of deep learning. The course really left me on the high and with a strong grounding to begin to press further in this deep learning journey.

By Assaf B

Aug 22, 2018

If you had Prof Ng's "Machine learning" in the past, you expect perfection, so you may say that this course had imperfections such as Jupyter work instead of offline work, which confines your creativity when working on an exercise, and the course bit short, even for a chapter in a specialization.

However, when comparing to other courses, to nearly any other MOOC except "Machine learning" and perhaps "Complex Analysis", this course is still a DEFINITE five stars course. In content, in knowledge bang for your time invested, in usefulness, in teaching ability, and the list goes on.

By Karim W E A

Aug 15, 2017

A lot of repeated material from Stanford's Introduction to Machine Learning, especially week 4. But of course, implementing all the assignments in Python, which is probably the most widely used language for ML and one of the most efficient ones as well; That was a big advantage over the material covered in Introduction to Machine Learning. Also, the material was explained in great detail and was tremendously organized. Would highly recommend the course to anyone who's looking into expanding their knowledge in Deep Learning. I can't wait to start Course 2 in the specialization!

By Fabian Z

Aug 5, 2020

This is by far the best course I've had. It's detailed, intuitive, well-explained, and engaging. This course kept me having fun all the time. The brief questions in-between the videos are amazing. The videos aren't really that in-depth mathematically but the discussion forum provides those additional details for those who need it. I also really like the fact that you guys added the heroes of deep learning videos, they really are amazing people and I think it helps me to get to know the field better. Well, I'm out of words. To summarize, it's amazing and I really recommend it.

By Ricardo S

Nov 24, 2017

I found this course to be a good introduction to neural networks and deep learning geared toward the uninitiated. For anyone with some experience however, the course can be rather easy, though it can serve as a review and it is fast enough to go through. I find it to be always good to start from basics, especially in the complex and always evolving field of machine learning, and this is an adequate starting point. I suggest that anyone taking this course with serious aims should seek to understand the mathematics introduced in it, though it is often mentioned as "not needed".

By Mark M

Jun 20, 2019

The programming assignments in this course provide practical experience in building a deep learning neural network. The lectures are thorough and easy to understand, and they connect clearly to the quizzes and assignments. I'm grateful that Professor Ng and staff put this excellent resource together and make it accessible to all. I currently work in Cambodia, where I hope to introduce courses such as this to young people who have no educational opportunities. I highly recommend this course to all who wish to be aware of the incremental significance of AI in our time. Thanks!

By Aayush K S

Apr 6, 2019

Really great course material. With minimal mathematics behind this, this course provided a great start to deep dive into deep learning. The video length and the quizzes and exercises were great. Also, since jupyter notebook was hosted by coursera itself, I didn't had to invest setting up infrastructure or downloading packages in my local system which was unlike AndrewNg's MachineLearning course which used Octave. This experience made completing the exercises more efficiently. helping me to utilize most of my time in solving it. Looking forward to complete the next courses.

By Matteo C

Mar 8, 2018

A great course.

The topic is very compelling on its own, but the magic is all in the instructor. Andrew Ng is passionate and explains complex concepts by slowly building up to them. It was very important for me that he introduced the math and notation required, without assuming a lot of prior knowledge.

The programming assignments are worked on and submitted with Jupiter notebooks, which is great.

To make the most of this course, I would recommend doing the "Machine Learning" course from Andrew Ng, as it has a lot of relevant content and a good refresher on linear algebra.

By Casey

Mar 7, 2018

Definitely recommended. I've taken various other deep-learning lessons and tutorials, but none of them gave me as much insight and practice as this course. I get the feeling that a lot of work went into the design of the course and even the homework problems.

A practical note for people considering the class: it'd be a good idea to review how matrix multiplication works before diving in, because that comes up again and again. There's a review in the course itself, but it doesn't come until week 4, and I found it necessary to analyze matrix dimensions as early as week 2.

By Abdur R K

Dec 24, 2017

Amazing course! I didn't even know python when I begun properly (only C++,C and C# and octave/MATLAB) but all the required functions/commands were introduced in a way that I faced no issues whatsoever. Of course I did need to google a lot of syntax differences (like for loops and stuff), but the experience was very fluid and everything connects extremely well to Andrew's famous Stanford ML course. If you're somebody who has only taken that course and are wondering if you can take the Deep learning specialization without having to study python first, I would say GO FOR IT!

By David P

Apr 10, 2021

Wasn't sure if I would like this going in, but I definitely recommend this to anyone interested in the topic, who has seen linear algebra and calculus before. Very well structured. It has been decades since my last "class" in calculus or linear algebra, but I was able to follow the math and instructions quite clearly. Instructor does an excellent balancing act, exposing the class to necessary details without going into unnecessary depth on derivations. Exercises are extremely easy but take you through all necessary steps, and you produce a simple, functioning classifier.

By Dimitar T

Jul 23, 2020

Andrew Ng is an amazing tutor and this is a great introduction in the world of Deep Learning. This course is for anyone interested in the topic, however, in my opinion it is advisable to first go through his ML course as this one feels like a direct continuation that builds on top of it. That beind said, the lectures are really well structured and the assignments are fun. One minor downside is that the assignments tend to hold your hand through the process, so to really test your knowledge, you may want to implement the algorithms on your own, using different datasets.

By Artur S

Dec 23, 2017

Very good course that can build understanding of neural networks and machine learning key concepts in a straight way. It is also interesting for some people, who thinks that he is advanced in machine learning, like me, but have only conceptual understanding of neural nets and no coding practice (just some experience with visual matlab plugin for NN).

Thanks for professor Ng and his deeplearning.ai team for preparing this course and for Coursera team for hosting it and making available.

P.S. It is so cool course that I'm helping with translation of this course to Russian.

By Francois R

Jun 29, 2018

The Super Excellent: How the course is built, with a lot of small block well placed on top of each others. The honest rendering (cutting over the hype) by Andrew Ng of DL and ML in general.The Excellent: The new notation and organization of the matrices (compared to Andrew Ng's previous Machine Learning course). The new explanation of backward propagation.The Good: The use of caches between Forward Prop. and Backward Prop., but also between the different functions. Note: The latter would benefit cleaner names and the usage of assert() on entry of the functions.Thanks,

By Victor D L M

May 14, 2019

Great introduction to Deep Learning and Neural Networks! I took the Machine Learning course offered by Stanford University and Professor Ng. and did not quite (fully) grasp what a Neural Network was doing. However, with this course, my intuition and understanding about Neural Networks and their inner workings was greatly enhanced. In addition, the course offers the most recent and best practices seen in the industry (e.g. introduction of the tanh and Relu activation functions ). I would recommend this course to anyone interested in Deep Learning and its applications.

By Abdelhak M

Aug 20, 2017

Hi Andrew,

It's just Awesome Andrew !.. it was a pleasure to achieve this course 8 years after I achieved your first course in 2011 (before coursera borns). Thanks to you and to all your team at stanford.

I can't wait for the next four courses :)

I was teaching the machine learning course to my students in the past 3 years and I plan to teach this current course to my students this year. They have the barrier of English language and I'm trying to do my best to explain the main ideas I understood from your course.

Abdelhak,

Professor at Mohammed V University

Rabat, Morocco

By Michio S

Sep 18, 2020

Prof. Andrew Ng made it easy for the beginner to digest the systematic discipline of Deep Learning.

In addition, I cannot say enough thank you to all those teaching staffs and other peers who helped me better understand the course through Discussion Forum. In particular, I would like to mention that my smooth learning progress owed to Mr. Paul T Mielke. Thanks, Paul!

After the completion of this course, I would like to view Prof. Ng's course CS230 at Stanford via YouTube videos to enhance the understanding of what I learned from the Coursera course.

Thank you very much

By Peter A

May 3, 2020

This course is a perfect introduction to neural networks. It builds on simple concepts to then put together larger processes that link and compound these concepts together. In all, the user begins to see how something like fitting a Logistic Regression model is not that different from some other learning models that are regarded as more complex, such as neural networks. Surely, as one continues, these things will diverge, but this course does a good job os using the knowledge users likely already have, in order to better introduce them to some more complex concepts.