Good introduction to implementing shallow and deep neural networks in Python. If you have no knowledge of neural networks or Python, I'd suggest doing a little preparatory study first so that you know what a neural network is and feel comfortable writing short Python programs.

Theory: the course is not heavy on machine learning theory. I had covered the theoretical parts previously in other courses. This course provided a useful summary of these and left me feeling confident that I had a good overview.

Maths: this course doesn't place great emphasis on the mathematics. It shows you the relevant equations, with the emphasis on understanding the underlying concepts rather than going through detailed derivations. Sometimes there's an optional extra video going through the equations in a little more depth. A frequent message is: don't worry if you don't understand all the mathematical detail, you can still learn to implement neural networks effectively.

Implementation: the course uses the Python NumPy library throughout. It does not go into deep learning frameworks such as TensorFlow or PyTorch. From the outset, you are taught to use NumPy in an efficient ("vectorized") way. The programming exercises are well thought through and I found that they all worked smoothly, a pleasant change from some other courses elsewhere.

Overall I found this to be a gentle but satisfying introductory course to the Deep Learning specialisation. Andrew Ng is an excellent teacher. His manner is both calm and enthusiastic and he clearly cares about equipping students with the skills that they need and doing so in an accessible way. The optional "Heroes of Deep Learning" interviews were particularly interesting, full of gems and hints about what could lie ahead if you decide to go more deeply into the field.

TL;DR: Very much worth taking if you're looking to get into the field, develop (much) deeper understanding of the underlying theory and the necessary infrastructure.

I first gave it 4 stars and then changed to 5, let me tell you why. If you're reading this review, you are most likely considering taking this course and you very likely have some idea about what Deep Learning is supposed to be. You're also probably aware of the "black magic" stigma surrounding the field and that it is going to take some time to get used to the way of thinking, even though if you have some experience in "conventional" machine learning. Well this course (read: it's creators) also understands all of those points extremely well. With that in mind, the course caters to people who are are making their first steps in the field of DL, people who are not expected to have a high degree of expertise in dealing with DL models and especially not in creating those. Students are expected to understand about 85% of the underlying theory in order to get the models working (the rest is mostly calculus needed for deriving certain more difficult gradients) and the coding assignments include a considerable amount of hand-holding. That fact made me want to say how the course was trivialized in a certain way, and it really is (but don't let this discourage you; you will still need to implement all of the key parts and do take your time to really understand what they do), but then I thought about that again and concluded that I most likely would have struggled to complete the course otherwise. Andrew Ng and the deeplearning.ai team had a wonderful approach to teaching this course, it kept me coming for more and I cannot wait to start with following courses in the specialization.

I found it very helpful as it confirmed most of the things I had already learned by doing deep learning projects on my own, as well as browsing additional literature on machine learning / deep learning and having done some internships where I had to apply these things. So for me personally, this course did not teach me anything ew, but organised and structured the knowledge in my head nicely by summarizing it very neatly. Also, some of the hints on implementation where helpful (like the numpy reshaping issue with arrays of shape (n,) as opposed to (n,m)). One thing I found is that deep learning can only really be understood if the covering of back propagation includes the low level derivatives + chain rule discussions; otherwise, you dont really "understand" whats going on. I appreciate that the course (just like the original "Machine Learning" one, which was excellent) tries to reach a broad audience that does not necessarily know analysis to the extent required for backprop, but maybe it would be a nice idea to include a "mathematician's point of view" on the backprop as an optional part. I found that in my personal studies, looking at backprop from the pure analysis point of view helped me a lot in "demystifying" deep learning and seeing it for optimization approach that it is. Having said that, I found the course very nicely structured, with very clear explanations and relatable applications. Thanks to coursera and Andrew for providing this great source of knowledge for free, I really appreciate these efforts! Sebastian

PS: I gave it 4/5 stars, but for some reasion the rating keeps getting stuck on 5.

This was a very well-thought-out course for beginners in Neural Networks / Deep Learning. Andrew Ng sets a good pace; I was able to complete each week's lecture videos and assignments in less than 10 hours. Lectures were always clear and often went over things which would not directly be needed for assignments, but which will be useful to anyone planning to do work in this field. Andrew Ng was also very good about explaining where the mathematical equations came from, while stressing that it's not super-important to understand fully where they come from, as long as you're able to implement them.

I should add that I'm probably not the typical audience for this class --- I have an extensive math background but only just started programming a few months ago. Python code was scaffolded and commented in such a way that even a noobie to programming can follow and complete coursework, and I can say I've not only learned about NN/DL algorithms, but also a good deal about programming in python as well. One major topic that still blows me away is the speed boost we get from avoiding for loops and using vectorization instead.

The post-assignment interview videos were also interesting. Andrew Ng would interview a guest 'powerhouse' at the end of each week, and the topics covered there often went way beyond the scope of this individual course, and gave a much more broad overview of where we are now and where we seem to be heading in the near and long term.

This is a wonderful course. I have been reading passively for about a year on resources related to ML and DL, but never got the full grasp of the concepts the way Prof. Andrew explained them. The quizzes where entertaining and insightful, as well as the programming examples.

I completed this 4-week course in about 2 days straight; some of the quizzes were 70/100 at my first try but then got to 100/100 after 1-2 tries. On the programming assignments I got 100/100 on the first try (except for the first one which didn't register my last 3 code answers -even though I typed and ran correctly - for which I had to restart the kernel, launched it again in incognito mode and after I was done re-ran all the snippets one last time just to be sure).

The hardest part of the course for me was to understand derivatives and overall calculus development and factorization because I had the necessary classes a few years ago, and honestly I wasn't very good at it back then either.

One thing I would suggest would be to improve audio quality, as well as editing the videos instead of providing a warning message before a video with errors because sometimes it's hard to follow the course part let alone spot the error itself.

Again, I would like to thank Professor Andrew Ng. and the Deeplearning.ai team, as well as the Coursera platform for providing such great realtime capabilities like the jupyter notebook and the automatic grading system.

This is a great course which covers some popular machine learning techniques such as regression. And then it moves to deep learning with neural networks with the techniques of forward and backward propagation. It is a good course for beginner and the homework are fairly easy. However, this course still leaves some unanswered questions which might be covered in the future courses such as how to select hyperparameters, why we choose this specific cost function, are there any other deep learning framework other than neural networks structure, any other application other than image recognition. In addition, for those who have some background in machine learning, the interview section is a bonus which talked about GANs.

There are somethings not very enjoyable as well. For example, the notebook is unable to download so people have to write it down otherwise when the course expired, it is quite hard to get the course material. The lecture notes are badly organized: you have to download every slides one by one. (or I just didn't find the right place to download). Thirdly, this course didn't talk too much about techniques that the industry is using. What I am trying to say is that I don't know if the techniques in this course is applicable in the industry, is it too simple or is it too old etc.

Overall, it is a very fun and educated course. I can't wait to jump into the next course.

This course from the basic to the advanced, leading us to understand deep learning. From the initial logistic regression, then to the shallow neural network, and finally to the deep neural network, we gradually learned the neural network representation and calculation process, and finally began to implement the cat image recognition binary classifier. The course is very clear and logical, eliminating the tedious mathematical derivation, but still allowing us to understand all the mathematical details including calculation and vectorization. The assignments are done step by step, starting from the basic functions and gradually encapsulating, and finally constitute a complete neural network, which enables students to have a deep understanding of neural network and master knowledge from practice. It is worth mentioning that the setting of course gradient is reasonable, and the details that are difficult to understand in the previous course do not need to be understood all at once. In the later courses, the understanding of the previous knowledge points will be deepened repeatedly, and due to the foreshadation of other knowledge points, a more complete and comprehensive supplement will be provided to the previous knowledge. Looking forward to series two.

This is my first completed course on Coursera.org!!! and I win a certificate with nearly full marks! I was very bad at coding .... although now I still not good at it, this course convinces me that even a poor guy in coding like me can finish and establish a neural networks! it truly boosts my confidence! Thanks Andrew Ng.

One thing to note for this course.... maybe it can improve later in this side. I feel quite easy and comfortable with all mathematical deductions.... however, when I code in week 4, the backward propagation, I nearly get lost.... I spend the whole afternoon and night to solve it and then finish assignment 2 in week 4 (from 2 pm till 10:03 pm)... I bet many people get stuck on that area. hmm, It's really easy to get lost and puzzled there. I guess there maybe some points we can figure it out more easily. Just a little advice.

Anyway, I love this course. It's a trigger for my coding area, though I'm a physics student and now in electrical engineering.... I still feel very comfortable about this course.

Thanks Andrew Ng!!! I don't know how to express my gratitude to you! If I did not take this course, I may never ever to attempt on Deep Learning, such a complex and advanced thing.

I will continue to study~~ see you

Pros: + You will understand clearly how things work and why they work

+ Provide mathematical insights for those who are interested (really a big plus wrt other courses)

+ Overall a simple introduction to Neural Nets. Even those who already have experience can benefit from this brush up (even at a fast pace: you can complete it in 2-4 days)

Cons: - Since it is quite basic material, those who are already accustomed to NN might want to jump to the second course of the specialization.

- You learn python by doing, but you will not get an understanding of python. I would suggest to get a little bit familiar with it in other ways (however, this is not a requirement to master the course).

Clear, quick overview of the basics of Neural Network. Provide even some mathematical justification, even if it is really not a requirment to fully understand it in order to succesfully achieve the course. However, IMHO it is always good to have at least some insights of the mathematics behind.

This course sets well the basics, but to be really able to work on your own projects I think it is a must to take the second course of this specialization.

This course is truly deserving of its high ratings. Prof. Andrew Ng's extensive breakdown of the structure and function of neural networks work is unparalleled. For me personally this course has been of great help. The theory lectures made me understand just how these networks "learn". This course is a great beginning and I think, prepares the student well to learn more in depth and advance concepts of deep learning.

However, if you are looking to get hands-on experience building and training deep learning modes I would recommend checking out some free resources on YouTube with the Keras framework. I played around with with Keras following the YouTube channel Sentdex's Keras tutorials. Then took this course to get a more mathematical and theoretical understanding. Some students might find themselves a bit unprepared for the coding exercises, since the lectures are more focused on theory and math, showing little to no code. This is why I thinks this is a great (if not the best) 2nd course, but maybe not as helpful as a first introduction.

For a free first introduction, check out the channel 3blue1brown's videos on Neural Networks to get your feet wet, before diving further deep. ;P

Dr. Andrew Ng is brilliant and it is so amazing to have access to this type of knowledge for less than I spend on Starbucks in two to three weeks. I am taking some online courses at a big name university (to the tune of $4,000 per course), and for the money, this is a real bargain and just as good if not better!

The thing I liked most about this particular course is that it showed us what's happening under the hood, and not just a course on how to use tools, nor is it all theory. Dr. Ng also introduced us to Geoffrey Hinton, the pioneer of backward propagation which was worth the price of admission alone.

That said, it was not so tough that I couldn't keep up. I would say that having some exposure to calculus would help, but it is not required. Also, you need to be more than just familiar with Python, but if you can spend a few extra hours per week on the course, you can work your way through it with just a familiarity with Python.

It has challenged me to keep going to the next level and complete the specialization -- AI is not rocket science -- at least not at the level of applying this knowledge. Being at Dr. Ng's level might be a different story.

Highly recommended!

To set the context, I have a PhD in Computer Engineering from the University of Texas at Austin. I am a working professional (13+ years), but just getting into the field of ML and AI.

I completed Dr. Ng's course on Machine Learning on Coursera first. I recommend that students of this course should first complete that course (or an equivalent one). This course was an excellent review of the basic concepts of Neural Networks. The lectures were well presented and the maths/equations were explained intuitively. The problem solving assignments were in Python (as opposed to Matlab). As before, Dr. Ng walked us through the assignments: hand-holding us through the solution. The quizzes were fairly challenging and helped me reinforce the concepts quite well.

I wish there were a few open problems (Kaggle style) at the end of the course so that the class students could compete with each other. It would be a good addition to the course. I would appreciate more real world examples throughout the course as well.

I look forward to completing the remaining courses! Thank you, Dr. Ng. Thanks you, teaching assistants. Thank you, Coursera. This is truly a wonderful course.

Thorough and simple explanations that help internalize the deep learning concepts. Video lectures are very helpful. Listen more than once to clarify concepts. Very useful jupyter notebook exercises with solutions that provide knowledge reinforcement. Vectorized form of deep learning neural network equations enable development of clutter-free and faster scalable solutions. Before taking the course refresh your knowledge of linear algebra esp. basic matrix operation such as matrix size, transpose, and implementation in Python via numpy such as numpy.dot for matrix multiplication, numpy.multiply for element-wise multiplication. Familiarity of Python key:value dictionary data structure and retrieval of values via keys. This knowledge will build confidence to code the functions and methods for forward propagation, back propagation, and gradient descent to update weights and biases. Also pay some attention to how indices in square brackets are used to identify matrices for inputs, outputs, parameters (weights and biases), activation values/models, various layers of a neural network, and nodes in a particular layer (all explained well in lectures.

I've been impossibly busy and first thought this was something i could play in the background while I did other work. Quickly it became apparent that data I had been used to with M.shape = (user/observation/etc, feature) was now the transpose. This took a simple few examples on paper to convince me why this was a superior notation for D/RNN architectures given numpy notation. I also at first thought that the bias should be added to W, X, for greater expressibility of the relationship y = g(WX) and for the backprop updates that require 'estimating' the W.T*g^-1(y) and g^-1(y)*X.T (where y is understood as the general activation after layer l and X is the general output of the previous layer), but now I see why separating the bias is useful -- it estimates the 'scale' of all the data at the output layer at once (estimating the unbalance in the marginal distribution, for example), whereas the other gradients come from estimating the perturbative deformation in the input layer, thus they are slightly different from the perspective of forward backwards distributional learning. Bravo, and thank you!

Thanks for the course. Very neatly explained on the background maths that happens in neural networks. This course will help you understand the step by step what happens within the network. The step by step procedure which is explained by Professor is great and he has repeatedly stressed the important steps to make it clear. Along with the explaining the formulas the assignment helps in implementing the formulas step by step and converting the whole thing to a neural network model, this is a great learning. One of the important thing covered in the beginning of the course is about vectorization, python broadcasting which is the key for neural network.

The pace at which Professor explained the concepts is good and easy to follow and the structure of the course is well laid-out which helps for the beginners.

One thing that could have been better is the assignments, current assignments are definitely helpful for beginners like me, but could have some more assignments which increases the complexity level (may be it is there in subsequent courses).

Overall very good course and helped me

I admire Professor Andrew Ng's patience in helping the students take baby steps by painting a big picture from each small pixel, just as how a neural network is built.

This course has given me great exposure to how neural network, although I realize I need to take a Python course to type code more freely and easily.

I'm going to do that next and then come back to the remaining courses in this specialization.

feedback - it's really hard to visualize some of these matrices and their dimensions used in a large neural network with so many parameters such as nx features, m training examples, n iterations, L layers with (nL, NL-1) weights, (nL,1) biases etc. I understand it's hard to show these matrices by writing as they are very large. I wish someone would develop a more "animative" way of illustrating these matrices that will make the intuition more stronger. for example, calculating forward_activation for all layers and all neurons across these layers by just passing X and parameters is a massive operation and the intuition stumbles purely by the scale of such a matrix operation.

Things that I learned: 1) Introduction to deep learning 2) Logistic Regression, gradient descent on logistic regression 3) Forward and Backward Propagation 4) Computational graph and how to use the computational graph to calculate the forward and backward prop 5) Shallow Neural Network, and how to work on it end to end 6) Deep Neural Network and it's end-to-end implementation on an application (Classification of cat vs non-cat). Pros: 1) The course is in Python 2) The way Andrew Ng sir teaches is simplistic and memorable as he starts from small concepts and relates the same concept in complex problems too. For example:- He started teaching forward and backward prop using logistic regression then he carried the same in shallow neural net and finally on a deep neural net. Cons: 1) The number of questions in the video is less as compared to the Machine Learning course by Andrew Ng. 2) There could have been more clarification on a few small topics. 3) The video didn't provide any sort of notes or lecture slides. ( Note that I found useful: https://lnkd.in/f3fZGCy )

As usual, Prof. Andrew Ng knocks it out of the park!!! He would argue otherwise, but he's a natural born teacher whether he admits it or not. This was a challenging course, but I found the objectives to be achievable with a bit of hard work and cool-headed thought. Having taken Prof. Ng's Machine Learning course already, most of the material from the first two weeks of NN4DL was review. Unlike the broader ML course, DL was much more narrowly focused on concepts leading to mastery of deep neural nets. It also ditches the MATLAB/Octave used in ML for a more portable Python environment. I had basically no knowledge of Python when I started, so I guess I learned it in 4 weeks! :D My advice: take ML first, or you may be lost. I had the math and ML background for this stuff to make sense, so Python was the only thing entirely new to me. If you're fuzzy on calculus, or ML, or programming, I don't recommend starting with this course. But if you have a strong background on those things, you'll find this course is well worth your time! Good luck.

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.

The content was very good and intellectually curated, and no complaints about a teacher of such high quality "Andrew Ng". Actually, I took the "Machine Learning" Course long before on Coursera from the same instructor, as I took this course now, which highly helped me to finish this in less than a week, although I never got time to complete the former course. Advice to any new students on this course would be to have a basic understanding of Machine Learning, which includes linear regression, vectorization et.al. , (or simply, "ML" course on Coursera).

One small amendment on this course could be to reshuffle the contents a little, from different weeks as I found the content which was in Week 4, to have high importance to be taught earlier in this course (for eg, getting matrix dimension right ), and there were others sub-topics in week 3 as well. I don't remember all of them, as I took 4 weeks worth of information, in just a single week :)

Very excellently taught, and contents, as well as assignments, were of topmost quality.

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.

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.