This course provides an excellent introduction to deep learning not skimping on the technical detail, while remaining succinct enough that the learner is able to follow fairly readily.

It does require at least a fundamental knowledge of Python, and a basic understanding of machine learning to fully engage with the content.

There hasn't been anything more enjoyable for me personally than to create from scratch (more or less) an artificial neural network and see it in action!

Excellent course focusing on the implementation details of multilayer neural networks. I find this course as a potential companion course for Geoffrey Hinton's Neural network lectures which provides you deep insights about various network types, and you can hear many stories about the early days of NNs from a cognitive scientist's point of view, but lacks of hands-on coding and practical exercises. The regular coding exercises in this course help a lot. Highly recommended.

Great Course, You get the theoretical concepts that makes you understand the underlining basics of neural network science. I have been extremely impressed by Andrew Ng's ability to make the explanation of neural network science so simple and understandable; additionally, I appreciate the opportunity to actually create neural networks from scratch. The interviews with experts involved in research into AI tools are also a great addition to this course; I absolutely love it!

Very nice course. Specially the way it has been developed is just what one needs to understand the functioning of neural networks.The notebooks are very helpful and descriptive and guides us to reach the correct code and understand the maths very easily. Couldn't have asked for more. This was my first course with Andrew Ng, and I really like the simplicity with which he explains the the various layers of the neural network. Simply amazing! Thanks for the wonderful course.

This course takes a step-by-step approach to build a solid foundation, then move on to elaborate on other essential aspects of Deep Neural Networks. The programming assignments are also very well prepared that started with implementing helper functions before constructing end-to-end neural networks. Optional exercise is also a fun way to apply what I have just learned to a topic of my own interests. Well worth the time and effort to learn from an inspiring master teacher.

Very clear explanations. Gives very good intuition of the math so that I could compute the derivations and convince myself of the math. The only objection I have for the course is that for the last programming assignment some functions were precoded but not revealed. You have to go into the supplementary files and look at what the function does. I think it would be best to add the functions to the assignment and preferably let the person taking the course write the code.

The course has helped me understand the nuances in Deep Learning and has provided a basic understanding of the topic.

The course was easy to understand and the assignments were structured in a way that would enable beginners to grasp the concepts easily.

Suggestion: I feel we can have a practice project (ungraded) that would bring together all the elements discussed in the course. This would be a self motivated project that could be showcased as a part of the coursework.

Thanks to the deeplearning.ai team for organizing this course!!!

Although I had zero experience using Python, I enjoyed this course. The videos were easy to follow and were great at breaking down concepts. I found myself going back to my saved notes on many occasions to refresh my knowledge or clarify my doubts.

The assignments, quizzes and lectures helped me surpass my goal - get the gist of neural networks. I now have the confidence to further my knowledge in this area.

Prof. Andrew Ng has done a great job by explaining the mathematics behind the workings of a neural network, and the programming exercises using python have been very well organized. This course definitely serves as an amazing background to other advanced courses in deep learning. I have thoroughly enjoyed and learnt a significant amount throughout the course. This course might also serve as a great refresher to those who haven't been in touch with the mathematics behind

The course is excellent. The programming assignments are well balanced.

They take you from basic python and numpy and they gradually increase complexity until you get two things: (a) good knowledge of feed-forward neural architectures, hyper-parameters, non-linearities, back-propagation, derivatives etc. (basically ML stuff) and (b) a principled way to code your models, which allows you to quickly switch to more complex network structures with minimal effort.

Thank you!

This is an excellent course for anyone who wants to know more about neural networks and gets some update of the latest developments since what was taught in Stanford's ML course. As in the ML course, prof Ng speciality is to make difficult subject so easy to learn and so accessible for everyone. Compared to other NN/DL courses/books, this course is relatively easy, and it's good for getting the overview about the subjects before jumping into more advanced NN/DL courses.

Skilfully avoid those complex math problems, Andrew gives us beginner a overview about Deep learning and its application. Also the way he picks up to teach us is very fascinating. Almost hand by hand teach us writing code, how to organize your coding structure, and step by step follow his teaching to build your own neural network. I can't say it's very technical dictionary designed for those old master. But if you're interested in AI, this course should be a good start.

It is excellent course. After done CNN network at my previous job used Keras, now I understand the algorithm behind the layers on the forward and backward , and how the cost function apply to the output layer, and gradient descent using in update the parameters. Also learn that vectorization is so useful for handling sample training set , units in the layer, and reducing for loop. In addition, learn python programming.

Thank you for providing this great course .

It is a really great course if someone wants to dive into the field of deep learning. This course provides sufficient introduction to theory of Neural Networks. Assignments are little bit easy, they only include implementaton of equations in python.

It would have been great if there was at least one assignment where students implement deep neural network from the scratch without help of any source code provided from course staff.

Thank You for such an amazing course!

I like the idea of building NNs from scratch as I believe everyone working in ML should understand basics such as matrix dimensions matching and back prop calculus.

Andrew's way of explaining ideas is very clear and concise as always.

Programming assignments are relatively simple as varieties of hints are being provided. Nonetheless, they still make me think algorithmically and analyse the procedures implemented.

Looking forward to other courses in this Specialization.

I honestly think that auditing the course provided me with a PROPER trial of the whole course. I would have liked it to be a bit more challenging (on par with University courses), with overall, it is a more than appropriate specialisation to consider.

I actually ended up purchasing a subscription for a month to gain the certificate :) ! I do not know how I am going to fair against the other courses in this specialisation, but I have been pleasantly surprised so far! :D

I love this class and Andrew Ng. I think Ng's class is very efficiency for a beginner to Deep learning. Because An usually talks in a gentle and slow way(which I prefer ;) ), this course is even useful for non-English native speakers as they can learn English in this class too.

Also, the programming assignment is very helpful for me to get a deep understanding of what Ng had taught in the vedio.

In a word, I love this course as it give me a key to deep learning and AI.

Everything was great, Except for two things! First, the grading system didn't work well, sometimes it just showed me my older grades or it wasn't aware that I have finished the specific subject. Second, the matching names of the variables helped me a lot on writing the codes, I guess with different names in different places I would have to think more to do the computer assignments. Anyway, had a great time and experience. Thank you for all your passion on this course

This course was extremely helpful to gain a basic understanding of neural networks. The lessons are very clear and easy to understand, and they provide all the guidance needed to successfully complete the assignments and quizzes. I'm looking forward to further enhance my knowledge by completing the remaining courses of this specialization as well. My heartfelt thanks go to the instructors, DeepLearning.AI, and Coursera for providing this amazing learning opportunity.

I have a great experience learning this course. As stated by Prof. Andrew NG, I came to know new things about the algorithms. I was familiar with the algorithms but still had an opportunity to learn new things.

The explanation about the code was extremely good. Assignments are really very well defined and worth learning. I am very grateful to Coursera as well for providing such an extravagant opportunity to the learners.

Thanks a ton for the course and financial-aid.

This is an awesome course, Ng covers all the material thoroughly. He provides enough guidance and structure to both make the assignments accessible and allow students to build their own tools from scratch. I've been working on doing each assignment in R on my own and building the tools to make up for the differences in how things are handled in Python packages vs R. I have definitely learned more here than in other University courses that went over these subjects.

Excellent overview of the basics of neural networks! Andrew provides good intuition of the maths and explains the ideas very clearly, making them easy to follow. The only minor criticism I would have is that I would have liked a little bit less guidance from the jupyter notebooks when implementing the code in our assignments. At the same time, I recognise the course needs to cater to learners of many different backgrounds, thus I have not removed any stars for this.

Andrew Ng and his team showed their teaching abilities again. This course teaches the fundamentals of Neural Networks and Deep Learning. The lectures are very clear and comprehensive. I really liked 'from scratch' approach of the assignments. Assignments are not so difficult but very instructive. Jupyter notebook is very easy to use. The test examples after each function are also very useful because I did not have to check all the code for a simple function mistake.

Thank you!

It was a great course. The only thing that bothered me slightly is that the coding assignments were so "closed" and didn't let me code freely. In addition, I didn't like the "bottom up" approach, i.e - first coding the basic tools and finally code the whole picture algorithm. I understand it is not easy to take the "top down" approach in an online course like that and appreciate the approach you took.

Thanks again, see you in the next specialization course!

In the beginning of anything there will be some getting used to the material, but this course really guides one to be successful with this newfound material. It's amazing how one can go in not knowing anything really about deep learning and after a few weeks and some effort their minds and outlook on life completely changed. Grateful for this course, looking forward to seeing more content and sharpening the understanding of Deep Learning and all that it can offer.