I really enjoyed this course, as far as my knowledge is concerned no other Instructor make this course as understandable than Andrew. Being a person from different background initially I am scared about gaining intuition about the topics but Instructor explains everything in a lucid manner. I am very happy that all the programming assignments are guided in this course, a person with introductory knowledge in python can attempt and gain good grades. I recommend everyone who wants to take this course upon interest can take without hesitation irrespective of their area of study. Thank you

The Neural Networks and Deep Learning class from Andrew Ng, deeplearning.ai and Coursera is very well structured and taught. I learned a lot and I am glad I was able to use calculus and Python to better understand what is going on underneath the hood with forward propagation, cost, parameters, backward propagation, predictions and more. Andrew and his team are exceptional instructors. The Deep Learning hero sessions are very motivational and inspiring. I also enjoy Andrew's sessions from Stanford's CS230 online. Looking forward to my next adventure in this Deep Learning Specialization.

This course is very complete and takes plenty of time completely covering linear algebra, calculus and programming. If perhaps a bit slow this certainly helps whenever there is something you might not understand at first. I have found the high quality programming assignments to portray exemplary structure in the code and a very accessible way to get some experience working with different machine learning models. The course is setup in such a way that it can be comfortably done alone (haven't really tried the forum). I feel well prepared to get to work on my own deep learning project!

Before taking this course I have learned Machine Learning, which is another famous course in Coursera, also taught by Professor Ng. My feeling is that this course is not as intensive as that one, but still I learned so many new stuffs which are extremely useful in my own deep learning projects. Before taking this course, I had zero coding experience in Python and so was really nervous about the programming exercises. However, the exercises are very well organized that I think every one can handle easily. So what I want to say is, don't say you can't do it if you never give it a try.

Not sure how other people would fare, but I felt like in order to have a deep understanding of what was actually going on, I needed to go study the calculus and linear algebra behind the material (which I had done previously). I know that probably turns a lot of people off and is why it's somewhat glossed over, but thought I'd just put it out there.

I will say that this course was super helpful with seeing how a theoretical understanding of DL translates into code. The coding exercises were 100/100. So thank you for that! :). Looking forward to the next courses in the specialization!

I personally enjoyes this course very much. I think the videos are pretty straight-forward. I really like how every video offers a very brief, yet incredibly detailed recap of what was completed previously and what is about to be reviewed. I'd personally prefer more mathematics, but Andrew provides the equations and optional videos. This enabled me to derive all the equations manually and to compare my results with the ones provided from the course, whereas people less keen on maths were not bothered by this. I would recommend this course to anyone wanting to start neural networks.

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.

The course was very informative. I have learned a lot about NNs and DL from this course. The videos were well made, however I feel that they kind of lacked behind in terms of the audio quality. This is not really a problem, as the material and the great explanation of concepts overshadowed this minor inconvenience.

The programming exercises were also very interesting. The informative text blocks really helped understand what was going on and what needed to be done.

All in all, it was a very complete experience. I would suggest this course to anyone trying to get into AI in general.

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.

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.

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

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...

I've followed similar courses in the past but I can say this seemed to be well structured for the broader audience. Andrew tries not to scare people away with big math and tries to focus on the principles of deep learning. I would highly recommend this course to others that wanted a deeper dive on this subject but are worried they don't have the smarts to do it. This is just challenging enough that you will learn, but not too difficult where you will feel like giving up. It was a good refresher course for me with a slightly different approach than what I've seen in the past.

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.

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.

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.

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.

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!

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.

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".

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!

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.

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.

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.

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!