I've taken and finished Udacity's Nanodegree, and while it certainly has a lot of its own strengths, I came here to get another perspective on the math involved, especially in backpropogation and numpy operations. Lo and behold, this class (Andrew in particular, of course) delivered exactly what I was looking for. And because the course was supremely self-paced, instead of feeling rushed to hit an official deadline like Udacity's course, I was able to take the time I needed to watch the videos a couple times each, when necessary, and really drill home the concepts.

Lastly, the projects here at Coursera are extremely well thought out, organized, and testable. I *loved* the use of the numpy seed operation, so when I completed a function and tested it, I felt extremely confident that the inputs, operations, and outputs were exactly what I needed. At this point, I definitely like the approach to projects much better than Udacity's (always felt like more of a guessing game there).

I'm excited to start and finish the next course in the Specialization!

Excellent course. Quick introduction to the basics of neural networks. This course has very high overlap with Prof. Ng's course on neural networks at Stanford. This appears to be the updated version on his new DeepLearning.ai platform.

The programming assignments are very user friendly, in that the code is already highly structured with student code just to fill in a few blanks. They also provide built-in test cases. The difficulty level is not high compared to a more open ended problem formulation (let alone a real world task). The assignments do make efficient use of student time in that they focus on the essential aspects of the course material and minimize time spent on extraneous computer programming challenges.

I appreciated the consistent and strategically chosen notation, which makes it easier to translate formulas into code snippets. Ng's notation conventions allow you to make an educated guess at how to vectorize algorithms in numpy simply by capitalizing variable names.

Thank you for sharing your knowledge and expertise with us!

This is a great class to get introduced to deep learning concepts and get some hands on experience with the underlying machine learning aspects. The Jupyter notebooks are great in that you are left with something you can use later as a starting point if you want to do your own implementations. The flip side of that is that, in my view, the coding assignments are made too easy and I feel that after all the hints and given the code you are given, the student's contribution is a tad too trivial at that point. Still, this doesn't change my rating because from the perspective of learning about DL concepts, this is not a crucial point. Since the course is still very new, there remain a few bits of consistency in notation and other little details that haven't yet been 100% fixed, but there's a lot of activity in the forums to help you clarify things and give feedback on what is not working.

As usual, Andrew Ng does a great job of motivating and explaining all the concepts. If you enjoyed his ML class, definitely go with this specialization.

Extremely well-taught and well-structured introduction to neural networks and deep learning. I found the explanations of forward and back propagation to be at a level suitable for getting the algorithms to work without swamping one in detailed calculus, but with enough detail to enable productive further study. There is an introduction to computation graphs that will hopefully lead into Tensorflow in the next courses in this specialisation. Professor Ng is a methodical, very knowledgable and interesting teacher and I really enjoyed all his video lectures. The weekly quizzes are reasonably challenging and the programming exercises very well written and enjoyable. If I have one minor criticism it is that there is perhaps a little too much 'hand-holding' in the programming exercises; I felt that some code was supplied that could have been left for the student to fill in, some very basic Python instructions could also have been left for the student. I am greatly looking forward to the next courses in this specialisation.

This is an amazing course which has helped me learn so many things about Machine Learning and Deep Learning that I could've never imagined learning/trying to learn on my own. Andrew Ng is a brilliantly awesome teacher and an amazing person! The way he teaches tough concepts so easily is beyond my comprehension, but thanks to him, I now have so much of a better understanding and intuition about these in-depth concepts of Neural Networks and Deep Learning. I've had a blast completing this course and its assignments. Each lecture was like a fun learning experience I kept looking forward to. I'll try my very best to leverage my learnings from this course everywhere! I'll be sure to complete the other courses in the Deep Learning Specialization series. Taking up this course is probably the best academic choice I've made in quite a long time - it has kindled my interest in AI and Deep Learning like never before! Thanks so very much ~ Andrew, Coursera and all the amazing people behind this wonderful course!

- Shakti

The best course in deep learning out there! Python assignments might be a little easy for someone with extensive knowledge of python. I don't think this is necessarily a bad thing, because it makes the student focus more on understanding deep learning rather than spending time writing python-based code (e.g. creating the functions, importing libraries, etc). If you don't have extensive python knowledge, you will be able to do the assignments, but I would recommend to try to understand how the whole code is structured and what each line of code is doing. Also, although Andrew continuously suggests to not worry about the calculus, I think it is helpful (and maybe necessary) to have some basic knowledge of calculus (derivatives) and linear algebra (vectors, matrix multiplication, etc) before doing the course. This will help you have a deeper understanding of the math behind the code and how neural networks work. Overall, Andrew is a great professor and covers all topics in a comprehensive and understandable way!

First of all, I thank Professor Andrew Ng for offering this high quality "Deep Learning" specialization. This specialization helped me overall to gain a solid fundamentals and strong intuition about building blocks of Neural Networks. I'm looking forward to have a next level course on top of this track. Thanks again, Sir!

I strongly recommend this specialization for anyone who wish get their hands dirty and wants to understand what really happens under the hood of Neural networks with some curiosity.

Some of the key factors that differentiate this specialization from other specialization course:

1. Concepts are laid from ground up (i.e you to got to build models using basic numpy/pandas/python and then all the way up using tensorflow and keras etc)

2. Programming Assignments at end of each week on every course.

3. Reference to influential research papers on each topics and guidance provided to study those articles.

4. Motivation talks from few great leaders and scientist from Deep Learning field/community.

The Neural Networks and Deep Learning course offered through Coursera and authorized by DeepLearning.AI was a comprehensive and well-structured introduction to the field of deep learning. The course covered a wide range of topics from basic neural networks to more advanced concepts such as convolutional and recurrent neural networks. The instructors were knowledgeable and approachable, and the content was presented in a clear and engaging manner.

The course assignments provided a hands-on experience for applying the concepts learned, and the discussion forums allowed for peer-to-peer learning and exchange of ideas. The use of real-life examples and applications helped to better understand the practical applications of deep learning.

Overall, I would highly recommend this course to anyone looking to gain a solid understanding of neural networks and deep learning. It provides a strong foundation for further studies in the field and prepares individuals for real-world applications of deep learning.

I really enjoyed this course. Andrew really knows this topic very well and his passion shows in his teaching. The course was structured very well and was very easy to follow.

I underestimated the knowledge of math required for deep learning. I was never very good at math and it really has been a while I learned vectors, matrices, calculus etc., but this course gave a nice introduction to the math that is needed. However, for me personally, I still had to go back and learn the basic math concepts. Khan Academy and YouTube videos were very helpful.

I am very good in coding. However this course made me realize that there is not much coding as such for deep learning. Python libraries really makes it easy. You need to understand the mathematics and formulas, and after that, its all about the test data and your hyper parameters.

Unfortunately I have to take a break as I have to travel for business, but I am highly motivated and I will definitely return and complete the other courses for specialization.

Excellent course with hands-on sessions. It is really difficult to learn neural network and deep learning with only theory part. Practice along with theory makes course very much interesting. In this course, Python is used which is open source and freely available. But it is difficult to execute downloaded iPython notebook as dataset "lr_utils" is not available. However I could execute the code with other dataset but it was difficult initially. There should be separate video lecture about explaining how to solve assignment because initially it was difficult for me to solve the assignment. Grader was giving grade 0 even though code was right then later I found that I was removing some of the lines in comment region and hence I was getting 0 grade even though source code was correct. So special session about submission of assignment should be there. This was my first course on neural network and deep learning and it was great learning experience for me.

Wonderful introduction to deep neural networks and the theory behind them. Programming exerices make for a fun way to try out concepts introduced in this course. Andrew has mastered the delivery of complex concepts and math behind neural networks in a systematic and discrete chunks, which allows for easier absorbsion of the material. One thing in particular that this course really shines at is looking at the computation graph of forward propagation and using it to explain derivatives used in backward propagation. This is one thing I missed in Andrew's Machine Learning course. Another subtle change which I found to have a big impact on the ability to reason about various computations in the choice on how to organize input and parameter matrices used in neural network modeling. I found the choices presented in this course a lot more intuitive than the ones in ML class. Many thanks to Andrew and his assistants for putting together this material.

Andrew Ng is the simplest, most genuine teacher available online. 3 years ago when I first did his course on ML, I was enthralled just by the way he 'spoke' and 'drew' Maths. However, it was still one of my first MOOCs. I really didn't have much to compare. Moving ahead in time, I did plenty of online courses, saw plenty of instructors and came across a hundred fancy techniques to make us learn. Yet, I could barely find the will to complete things in time. Lately, I thought of trying my hand at core DL and I returned to NG, except this time I was deeply apprehensive and mostly half-hearted.What followed was pure magic. With just a digital pen, a writing pad and plain-old slides, NG explained some of the most intricate nuances of Adv. Maths in minutes. His ways were older than the whole damn digital age but still more effective than all the jabber around. I couldn't get up for hours at a stretch.Nothing, nobody, comes close to him.

An excellent introduction of the basic building blocks.

In terms of constructive feedback / areas for improvement. I found Python/numpy matrix & vectors still caused a little frustration in the first coursework and whilst I appreciated the extra section on gotchas with vectors / rank 1 arrays and keeping things as columar/row matricies - it might be worth bringing forward the notes on cardinality and matrix multiplication earlier in the series, as I personally missed the transpose being required for the cost function in the logitsical regression coursework of week 1; ending up writing as: # ensure vector dot products - otherwise it seems we need to do a transpose (?!) positive_diff = np.dot(Y[0], np.log(A[0])) negative_diff = np.dot(1 - Y[0], np.log(1 - A[0])) cumulative_diff = positive_diff + negative_diff cost = -cumulative_diff / m rather than: cost = -(np.dot(Y, np.log(A.T)) + np.dot(1-Y, np.log(1-A.T))) / m

This course is introductory. It gives a broad overview of deep learning using a hands-on approach, i.e., it instructs you how regular and deep neural networks work in practice. As such, it is a good option for both novices in AI and IT professionals (my case) to catch up fast with the fundamental concepts of connectionist models. Andrew Ng has a lot of fluence in the field and is pretty charismatic, so that the classes flow naturally and get the attention of the learner. I can honestly say the course made me a better computing professional (because I learned I bunch of new things) and a better teacher (I teach computing at a technical school in Brazil). A piece of advice: if you intendo to apply for this course, you do have to have some mathematical skills (a bit of algebra and calculus is mandatory, basic statistics aids a lot) and mid to high level programming knowledge (if you know Python, the course will be a bit easier for you).

The first course is very good for beginners, however if one has no background skills on how to program in python like myself, then this course is a bit challenging. Implementing all of what I've learned to the Juypiter Notebook using python 3.0 was a bit tricky but understandable as you learn. I feel happy and motivated to continue and finish the whole specialization course. I have a strong background in integration calculus, but because the last time I had to do calculus was years ago, it was also a bit tricky to understand some of the calculus concepts presented in the course. I think for the first time user, it will be highly advisable coming from my own thoughts that the student learn Calculus mathematics first and as well as the python specialization course before delving into this Deep learning course. I know the lecturer mentioned that it is not necessary to know Calculus maths, but personally I feel like people need it a lot.

I liked the course very much. The videos and steps to get me to the point that I can really implement the concepts was very much helpful. Although I feel that I need more practice by programming. I think it would have been better if more programming practices provided.

Many of the programming parts that was related to the preparation of the data was provided. Maybe it could be beneficiary to do those parts on our own as well.

The forum is so crowded and hard to find my way around. Maybe something can be done about that as well.

In general I really liked the course, and I think it was the best way to learn the Neural Networks. Now I feel more confident to dive into text books and more mathematics of the NN. I would also like to add that I really loved the "heros" part. Get to know the community, history, and learning about the way that the pioneers and creators of a topic think was very helpful for me.

Thank you and good job

Novin

Dr Ng is an outstanding teacher. I like that the material was presented gradually and incrementally, without large gaps. I never felt like I was thrown into the deep end and forced to fend for myself, like I did in courses from at least one Coursera competitive. On the few occasions that I ran into problems with the assignments, browsing the forums was really helpful. With so many people in the class, there was always someone else who has run into the same issue I had experienced. Mentors are pretty diligent about responding to questions. I still struggle a bit with the math since it's been 20 years since I've had it in college. Wish I were still able to derive the equations Dr Ng used. It is great that Dr Ng provided derivations as optional lectures. One issue I have is that the choice of layer sizes hasn't been covered. Perhaps, it'll be covered in future courses in the specialization. Thanks.

I have taken a couple of Neural Network classes at university level for my master's. I did learn a lot but this course on Deep Learning introduced me to concepts I had never had the chance to encounter in those classes. I enjoyed taking this class as well working on the assignments. The assignments are excellent even if most of the coding has been done for you. It is up to the student to understand the underlying code and to pick up Python if she/he has not encountered Python before. In this course, it is important to understand the core concepts before progressing to more complex concepts. I found myself frequently getting lost and having to revert to earlier topics to understand later topics.

It was a pleasant experience working with Jupyter notebooks, something I did not have the familiarity with.

Kudos to Andrew and team for making this course an enjoyable and rewarding learning experience.

Have you taken a course and has no idea what the lecturer is talking about ? If yes, I am happy to report that it is not the case with this course.

The materials are easy to follow and the video lectures's pacing is perfect for anyone with no experience with neural networks. They are well designed to help students to understand the basics of Neural networks by keeping materials focused but yet detailed enough.

Also, I have to applaud to Dr A. Ng's lecture delivery. Never once would he make students feel lost or discouraged, and he drop little encouragements along the way. It is like preventive-medicine, in the sense that he anticipated and took measures, to allow students to stay engaged and interested. Kudos !

TLDR: For anyone who has little to no background in Machine Learning and is interested in understanding rather than just knowing the basics with Neural Network, this course is for you.

The prospects of deep learning is exciting in every field from science, engineering, medicine, economics and many more. If you have any interest in Neural networks and Deep learning irrespective of your academic background, then this specialization will be a great opportunity to you for learning and harnessing the power of deep learning in your field.

The best part of the specialization are the programming assignments which are based on building and implementing popular real life applications of deep learning. Even though this may seem tough, you will have to fill only the important snippets of the code(the rest is already there for you), which makes it intuitive and easy. I used python for first time in this course so the course also became way to learn python. Very well designed course structure through out the specialization! It's a great way to introduce yourself to Deep Learning.

This was really good. Well paced and thought out. Paid attention to explaining the underlying fundamentals of math as well as the required Python programming elements. Important intuitions on how things work were useful for understanding the greater scheme of things. Also enjoyed the weekly "Heroes of Deep Learning" videos.

I completed the inaugural cohort of another online deep learning course and whilst it covered a lot of great material and current research in a short time the pacing was often too fast and as a complete beginner I was a little overwhelmed; feeling like I was always missing key concepts. I also found that Andrew Ng's videos contained less about personality and hype and felt like they were on a more personal level than some kind of mass market video.

I definitely feel like I've learned something useful and I look forward to the other courses in this specialisation.

This course was phenomenal!

First I want to thank Professor Ng and the teaching staff as well as the Coursera team for providing such a great quality course.

I had taken the Machine Learning course by professor Ng before which was a great course itself but I had still some issues with backpropagation. (it was a little bit complicated) In this course, on the other hand, the professor explains backpropagation and the math behind it in a lucid, simple way.

Using python as the course's programming language was excellent. It is in my opinion what makes this specialization an absolute winner. The course's assignments and quizzes would make the concepts of the course even more clear.

The interview with heroes of the deep learning section was a great idea, professional people talking about how they got where they are and advising beginners on how to thrive in this path is really helpful.

Amazing courses that go into detailed explanations about the math and intuitions behind the algorithms without getting too convoluted or making things unnecessarily complicated just for the sake of it.

Prof. Andrew doesn’t just tell you the name of a function for a library (like scikit

learn or tensorflow) and give you magic numbers for parameters. You actually build the model yourself and learn what the parameters stand for and what is the purpose of those parameters and hyper-parameters.

The specialization is well divided into meaningful courses and each course is well structured so that you know exactly what you are going to learn and what key specific skills you will get after completion of a course. Because of the quizzes and practical labs, after completing a course you actually gain confidence that you can design optimized solutions for that particular set of problems.

In the deep learning specialization provided on Coursera, you are taught the theory by professor Andrew Ng, who is the Co-Founder of Coursera and has headed the Google Brain Project and Baidu AI group in the past. Professor Ng teaches in a very relaxed and patient tone and the explanations are clear and well formulated. One of the major upsides I liked is that the notation used is carefully chosen and very clear. Professor Ng makes sure to reference the most important scientific papers that contributed to each idea, which is great if you want to dive a little more into details. To progress in the course, at the end of each major chapter you will have to submit a multiple-choice quiz and one or two programming assignments in python. The programming assignments require you to complete a 3/4 finished code, and the focus is on understanding the concept and not on programming.

Easy to follow. My previous knowledge of calculus enabled me to verify some of the statements on my own which gave me a deeper understanding of the limitations and opportunities in the neural networks. However the training was designed so that I believe a person with zero calculus experience would learn how to write and run the code and feel they understood a lot more about deep learning.

Its incredibly rewarding to learn a skill that take you over the buzz-word level. This training gave me enough to have an intelligent discussion with industry experts, and even propose changes in algorithms that they had not considered them selves. This is more value than I expected. Granted, I spend quite a lot of time revisiting the material presented and making my own analysis during the course, but it would never have gotten to this level without Andrew Ng. I am totally impressed!