Some coding assignments were too hard to follow what was required.

Assignments were extremely didactic; there was no room for creativity. They were not transparent and gave a minimal idea of how to implement these things properly. Course moderators did not bother to answer any of my queries, making the course even less intellectually stimulating. The lectures were monotonous, and hence, I was having trouble finding them to be very engaging. Although, the professor did give some insightful points.

In conclusion, I wouldn't recommend this course to someone unless they are extremely novice programmers. Yet, one may refer to the videos to gain some conceptual clarity on specific topics.

This course felt rushed. Especially, the programming assignments, which had many errors and were frustrating at time. It is still worth it since the content is really good -- only if you are willing to go through the frustration during the programming exercises.

This one went a little fast for me, can't say that I'm confident on the shapes of tensors going through RNNs and why

Many topics are only quickly skimmed over. Serves as an overly brief introduction to RNN.

This course has many inconsistencies and errors in the homework. Seems like a rushed job.

some optional exercises are wrong, wasted a lot of time on LSTM backward propagation

It was difficult to follow the programming exercises because many of it had already been written. I think it would be more useful to learn one framework instead of using both keras and tensorflow in one course. I still don't know how to debug any of these frameworks. Without the forums, it would be very difficult to pass the assignments. Sometimes there were bugs in the jupyter notebook, sometimes typos that were misleading. As a result, it took me many hours stuck on one assignment. It would be good if these comments are taken into account for the future classes of this course. I really enjoy Andrew Ng.'s courses but I was disappointed at this last course's assignments.

There are a lot of mistakes in programming assignment. Please update and fix it

Wonderful end to this Deep Learning Specialization. The programming assignments cover up a variety of hot topics in the Deep Learning market. The videos are very well made and teach the content in depth. A special thanks to Prof. Andrew for yet another amazing course in this wonderful specialization!

Great module, I am lucky to have used this resources in learning sequence models, I can imagine running LSTM using one of the frameworks without ever implementing it myself, Andrew Ng/Deeplearning.ai is the best!

Professor Andrew is really knowledgeable. I learn a lot from his lecture videos.

Very good course which gives a nice intuition to sequence deep learning modelling. Unfortunately, this is the weakest one among the whole specialization. There are no deep explanation of LSTM as well as GRU and back-propagation algorithm. Seq2seq models explanation is not clear and looks too inconsistent. I had to read a lot of the additional materials and blogs in order to understood a theory behind lectures. Hence, the first week assignments were disagreeably difficult to complete, whereas second and third week assignments were comparatively easy. I think this course should be revised or prolonged for 4 weeks to cover LSTM models more profoundly. Nevertheless, I would like to thank Prof. Andrew Ng for really great job and initiatives in such an important area of study!

Little bit math heavy. It was sometimes hard to understand the intuition, e.g. RNN, LSTM, GRU

Could have been more thorough like previous courses

I went through all course of DeepLearning.ai Specialization. While I was more, or less happy with first 4 courses in this specialization, I have a very bad impression regarding "Sequence Models" course. Actually, quality of the courses is gradually declining, starting from 5 stars for very first course in specialization, ending with 2 stars for "Sequence Models".

"Sequence Models" course is *disappointing*. It leaves you with bunch of scratches on the surface of technology without any details and/or understanding of how technology works. In simple words, it is not the course, it is bad overview of just few technologies. In order to get similar comprehension of technologies delivered in first four courses of this specialization you will need to spend a lot more time digging for information elsewhere.

Same issue with homework assignments. They are a bit helpful for technology understanding to some small extent, but they do lack depth.

Whole "Sequence Model" course looks like compromised/failed attempt to explain fairly complex material to newcomers. As a result, newcomers won't understand anything due to complexity of the matter, experienced engineers won't take away anything due to simplified explanations and absence of details.

If you do not plan to get a DeepLearning.ai Specialization certificate, I do not recommend to buy this course. Unfortunately, it will be a waste of your time and money.

If it could improve assignment accuracy, it will be better

there are a lot of mistakes in programming assignments.

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.

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.

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