The sequence models course was the weakest of the 5 in my opinion. I did not really understand the details of the transformer model.

The programming exercises could be better. We had to code a lot of very different stuff without any foundations in tensorflow and keras. My intention to start this course was to get the theoretical background (wich I did) and to learn to apply it using python and tensorflow.

I hoped that I would be able to program my own models after finishing this course but I have to admit I can't. It is not very helpful to follow coding instructions line by line without knowing WHY it should be done this way.

I ama software engineer and don't know how others dive into this with little or no programming experience.

I would really like to give this course 5 * but the finally programming assignment was a disappointment. It seems many other folks feel the same way. I found myself spending many hours trawling the the web for additional background.

Week 4 coding exercise is incomparably harder than any other in this entire specialization.

The transformer subclass programming exercise is super useless task. Spent hours on this task and learnt nothing.

If it could improve assignment accuracy, it will be better

there are a lot of mistakes in programming assignments.

The good: Videos with a great overview of the theory, and some of the applications.

The bad: Everything else, I wouldn´t recommend this course. The quizzes are labs are very buggy. Labs are boring and very rigid (maybe because of coursera's platform?), I don't see the point of keeping testing (through all 5 courses in the specialization) if the student is capable of replacing a None in a variable declaration or a loop, while the whole preprocessing of the data is done automatically. Also, I found out that the last lab was literally taken from the tensor flow examples page and they just changed some variable names or replaced variable values with None. The change of paradigm in the last lab is also hard to understand. It seems they needed to "fill in" some content in order to "update" the course, I felt very dissapointed since transformers was something I was looking forward to learn.

After 5 courses the classes become very tedious, and the way Andrew present seems very old-fashion for a topic in the technology field. I mean, there's more appealing ways to teach computer/data science than with power point slides while writing on top of them. Youtube is full of videos on the topics covered by the specialization that show more passion for communicating than these courses.

The course overall taught me new things, but I am still kinda unsure how to exactly use it.

The exercises and explanations weren't as enlightening as earlier and unfortunately left me rather confused, despite passing 100%. You definitely need to consult a lot of other sources for understanding the topic.

The last transformer exercise left me stunned though in how bad it was. When I understood something it contained obvious mathematical inconsistencies. It was the first time I needed the forum help, which is outside the coursera website and they force you to sign up in addition to coursera.

The tutor reacted fast but extremely patronising, going so far as pretending mistakes in the exercise didn't exist, but very eager to blame me for using an outdated version, that I wasn't using.

Did not enjoy the experience.

Oversimplifying material, such as not showing any math foundations and proofs, does not lead to an intrinsic understanding of the material as well as fill-the-gap assignments do not enhance comprehension.

I understand that the course is intended for the broad audience but will one be able to implement those Keras and TensorFlow algorithms on a moderately complex problem, which is the ultimate goal of these courses? Highly doubt it because the code is pre-written for students and step-by-step guide is provided. In my opinion, one could go straight to assignments and induct / deduct the answers.

I love Dr. Andrew, I seriously do. He has inspired me in the field of Deep Learning like no one else did. But I detest how this course is made so expensive and in a wrong direction. I subscribed and paid $225 and I still was not given a decent amount of time to finish the course, even when I asked for extension. If this is the way these courses are, its better of learning from youtube. It is not worth the money

Dry and uninformative. Immense space for improvement. Corrections should be made to videos instead of having numerous revisions comments after lecture. Some variables introduced haphazardly. Notation not explained well, some clashing with linear algebra conventions. Coding exercises are elementary hyper guided.

Poor submission system. Failed many times to upload and had to redo the assignments. I was using a 250Mbps high speed network. Also course materials are problematic. The instructors are not willing to fix the problems for many years.

Week 4 is impossible to follow both in lecture and in the assignements which is more than likely done intentionally as this is the last assignment before you can stop paying their monthly fee.

Clearly the less structured and more confusing Course within the DeepLearning Specialization, a bittersweet end to an otherwise great set of courses. Quite disappointed.

Programming assignments are completely useless.

What to say after spending this whole weeks in the digital company of Andrew? :) Well, I have to words but to thank Andrew and the team for making the effort and putting all this together and pulling it off the way they did! I did already the NLP Specialization of DeepLearning.AI and wanted to learn more about the inner workings of neural nets and I was not a single minute disappointed by my decision! Extremely well done overall! In particular the course on Sequence models is very enjoyable and will give you all the tools and intuition necessary to understand the background and the basis of such models and how they work! You even have a last week on Attention-based models, which is a little bit introductory, given that Attention as a concept exploded after this course was developed. Nevertheless, it is very well executed! My only critique is that, the whole specialization was developed before TensorFlow 2.0, and later on it the assignments were ported and Keras is used. But during the lectures on will not find any introduction to Keras and all this content is left for the assignments. This requires from the student more effort to understand and use Keras. What can be a bit frustrating, because Keras API also evolved during this time. I would suggest adding a couple of videos or ungraded labs in order to teach the student a bit better how to use the framework. But well, this is a minor issue. 5 Stars are for the overall amazing presentation of what is indeed important, the Deep Learning fundamentals. Moreover, Andrews last video is just very much aspiring and a powerful message that everyone in the field should come in contact with! Congrats! I hope the team plan to execute soon an updated version of the specialization in order to incorporate the new advances and to adapt it to the newest DL Framework. Apart from that the theory videos are 5 out of 5. Clear recommendation!

A year ago I was basically "on blank" in regards of Machine Learning.

I've started "my journey" on ML about 9 months ago, with a text book I've got on Amazon called "Data Mining, Practical Machine Learning Tools and Techniques", Self taught I've read, transcribed, done some math, covered the half of it. But I needed something more practical to speed up, so I've tried also with the coursesfrom "Super Data Science"'s team on Udemy, but found them to be too focused on practice rather than deep reasoning of it (I might be wrong but that's the impression I had); So I needed more formal, University-like.

I've decided to try out Andrew's first course on Machine Learning (with Matlab), which gave me much greater view and understanding, had my head melting specially on weeks 4-6, but after finishing the course I've felt I did finally know what ML was! but still there was "a lot missing", given the course was already a bit old, and the technology had developed greatly since then.

Fortunately to me, I've found out about this specialisation right after I've finished the first course and I've signed up immediately. Today (14.4.2018) I've finished the second specialisation. After 6 months of continuos dedication, doing the first 3 month course, plus this 3 month specialisation.

Homeworks in Matlab and Python were my next challenge, even I'm a developer for 15 years (C# / Java, C). Combining a lot of new theory in a new language made it harder but also satisfying.

I'm the kind of person that needs to understand why things work as they work, that might be my weakness but also my strength; It's not enough for me to drive the car, but I need to know how to tune it. I must tell that for example, a video/lecture of 15 minutes meant to me usually 60 minutes of work, transcribing, doing the math, etc. That made my 6 months particularly long..

This is again a fantastic course and what a nice way to finish the Deep Learning Specialization. It is certainly the most difficult one from the whole specialization and has taken me a lot longer than I planned. This is partially due to the fact that focus is shifted a bit more towards the programming assignments and concepts that are only briefly mentioned in the lectures turn out to be crucial for the assignments. The forum helps a lot, without it I would not have been able to crack the first week, especially the optional parts of the assignments. There were also a few errors in derivation formulas, that had set me back, but in the end I understood the concepts a lot better and found some nice complementary resources online. And the RNNs are more complex and seem more variable than other network architectures, so that is ok that this course is more difficult. Now I feel that I finally have a good grasp of Deep Learning concepts and have a nice set of skills. And the assignments are super fun and very useful. Thank you Andrew Ng and your team for making such a wonderful content. I teach at the university-level and I can only imagine how much effort goes into preparing such a course and at such a high level of expertise. I encourage everyone to take this specialization, this specialization is the main gem in Coursera, in my opinion.

It is apparent how much thought and effort has been put into creating these courses. Dr. Ng introduces you to state-of-the-art CNN and Sequence models which are quite complex. But he expertly presents it to you so that you can focus on the essential aspects and not the details. In courses 1-3, you might feel like you're being spoon-fed in the assignments but it is really a great approach to ease you into the deep learning field. In courses 4 and 5, there is less guidance so that you can become more independent and be able to figure things out on your own. After all, this is how it will be in our future jobs - no more TA's then.

One thing I really appreciated in this specialization was the use of good notation. For me this was very important because it made it easier to apply theory into practice (via the assignments). Another thing is the amazing selection CNN and sequence model topics that were covered. Because of this, I now have a good idea where to focus my future projects/work. I also loved the assignments because they helped me understand the concepts much better.

For future students, please note that there are mini tutorials for Python (in Course 1), TensorFlow (in Course 2), and Keras (in Course 4). Keras is used a lot in Course 5 but there is no Keras tutorial in that course.

From my perspective as a learner, there're two biggest"disaster" they may encounter:

1. A boring lesson to take.

2. Tuns of confusing question hanging in mind with no way out.

Coursera prevents those two biggest obstacles of learning from happening. Here's my experience:

1. During taking the DLS Courses lessons, I burst to laugh more than some times.

The laugh comes from the joy of meeting something interesting, from the surprise of finding something very powerful, from the happiness of mastering something that can create everything, which is like a pencil to draw the boundaryless dreamy beauty world, a piano to compose infinite wonderful music, a magic to perform the impossible miracle.

2. The discourse forum is turning the potential disaster of questions into the joy of exploring and gain of problem solving.

First, mentors response questions fast.

Second, they analyze questions very patiently and carefully, no matter it seems big or small.

Last but not the least, they encourage a lot, which cultivates a free environment to ask, to explore, to experiment, and to share.

Here I give all my credit to the lesson team and the discourse team, they're the best dancing partner to each other. And now we learners are joining this wonderful dancing floor! There surely is lots of joy and gain.

I really enjoyed the course. As usually Andrew and his team of dedicated professionals did a wonderful job of explaining an otherwise very hard material in an accessible way. The distinction of Andrew's classes is that they really give the *intuition* about why a particular approach works. Sure I may forget which particular regularization methods exist, but I will remember *why* and *when* to use regularization. The details can be always looked up elsewhere.

I can't imagine how much effort it took to create high quality slides, transcripts and WELL-DOCUMENTED CODE(!) in the notebooks. Being a software engineer, I can't stress the importance of a good documentation enough.

Since the notebooks already propose a well-designed NN architecture which gets the job done, what I'd like to see is maybe some reasoning about why *this* particular design was chosen, and not some other one. There are some explanations already, but even more explanations would not hurt :)

That said, it is an amazing course, so I can't recommend it enough! Thank you!

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.

This review applies to all of the courses in the Deep Learning Specialization. First, I want to thank Professor Ng so much!!! This Deep Learning Specialization was fantastic!! I feel more proud after completing this than I did after finishing the CPA exam!

I took Professor Ng's Machine Learning course as a prerequisite, which I would recommend to everyone before diving into the Deep Learning Specialization. The switch from Octave to Python can be a little tricky, but stick with it. Octave allows you to gain a deeper understanding of the Linear Algebra aspects and matrix multiplication than Python does (for me it did anyway).

The entire line up of courses prepares you so well to develop an eye for deep learning use cases and gives you the skills necessary to dive in and start applying deep learning solutions to real world scenarios.

I'm so proud to have completed this specialization and I cannot wait to start building my own models and come up with ideas to benefit society! :D

With Gratitude,

Justin

A terrific set of courses that builds deep learning skills in neural networks. The course guides the student through various time based models to address how speech recognition, music generation, sentiment classification, machine translation, video activity and name entity recognition.

The journey includes Recurrent Neural Networks (RNN), Language Models and Sequence Generation for NLP tasks, Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Bi-directional (BRNN), Deep RNNs, Word embedding for NLP, analogies, GloVe, Sentiment, and de-biasing. The final week includes Sequence Models with Attention, BEAM search, BLEU Score, Speech Recognition, and finally trigger word detection.

The course takes works, attention to detail, patience with the programming exercises, and diligence in completing the videos, quizzes, and coding work. Highly recommend this course for the intermediate level ML practitioner that has Python backgrounds and wants to get a TensorFlow and Keras introduction

Dear Prof. Andrew,

it is with great gratitude that I leave you this message. After following your Deep Learning specialization, I have finally reached the level that will allow me to reach my goals in my projects, something I thought complex to do in 5 years but I did it in a 2 month interval. Your specialization in Deep learning is in my opinion the raw material to explode in AI. Each one of your 5 courses is like the meal that you never end even if you eat it all your life. I hope I'm not the only one of your students who has this enthusiasm, however you have already received many testimonials about your courses on coursera of which you are a Founder. Thank you so much for giving me a meal whose appetite never ends, thank you for giving me 80% of the subjects that are my goals. Thank you for Coursera. Every time I start watching one of your videos in the course, I want to stay there for as long as possible, thank you for making me love AI again and again. May God bless you infinitely