In the beginning, I found the instructor a little difficult to understand, even though he is very good at explaining complicated concepts simply. I am sure part of the reason is that I was unfamiliar with the technical terms. Once I switched on the captioning option, my comprehension improved however I noticed an average of at least one translation error per video and these seemed to be caused by the instructor's accent and were sometimes very interesting errors. So, I guess the system could use a little more training with the specific AI vocabulary and/or adjusting the context error settings for the subject matter.

However, once I had the captioning on, it was harder to follow the notes because sometimes the important information was right under the captions. What was really helpful was when he summarized with typed versions for two reasons. One, it was clearer to read and understand. Second, it was higher on the screen and did not overlap with the captioning.

This is definitely a top-flight course and supremely useful! I learned many new things about practical applications of recurrent neural networks in this class and found the natural language emphasis to be very useful, particularly for certain problems I have been working on for some time! Professor Ng's lectures are very well-organized and clear and follow a very logical sequence. The assignments, especially the programming assignments, are well designed and do a very good job of building upon what is taught in the lectures and add a great deal of value to this class. I especially like the fact that we worked so much with Keras, which is an important framework for building Deep Learning systems and which is so widely used (it is the framework I often use in my own projects), and I acquired a lot of new knowledge about Keras thanks to this course. Overall, it was a superb learning experience, and I will be recommending this to both friends and colleagues.

Good set of courses on Deep Learning. Some small complaints / recommendations:

- Courses don't teach enough Keras & Tensorflow syntax to be completely stand-alone. If you take this course, you won't really be able to build your own DNN's unless you also take a separate Keras / Tensorflow course.

- Links to Keras documentation are broken -- they now take you to the general Keras homepage, not the specific command's page.

- In later courses, Andrew Ng's lectures are not edited. Starting around the 4th course, you start hearing Dr. Ng stop and repeat portions of the lecture, presumably intending the first attempt to be edited out in the future. Usually this is easy to ignore, but in some cases he repeats 30-60 seconds of lecture, which can be confusing.

- In the last course (sequence models), the text captions of Dr. Ng's lecture have a lot of mistakes, which is a little ironic for a course on speech-to-text

During the past couple of months, I worked on this Deep Learning Course Specialization Course by deeplearning.ai (through Coursera). I think is a great course for everyone that is interested in learning more about this topic and not only the theoretical aspects but also from a practical point of view. Andrew NG does an excellent work by going through the theory and then leaving some time for the practical exercises which are the best and, at the same time, the most challenging part of this specialization. These exercises start with very basic stuff but quickly turn into interesting problems related to convolutional neural networks, face recognition and end up with sequence algorithms for natural language processing.

If you are interested in building your own NN algorithms, learning about Keras and TensorFlow and spend some time working on applied exercises then I would recommend you this course!

I'm Zeyad, an undergraduate of Computer Engineering at Alexandria University in Egypt.

Taking this course really helped me to learn and study this field and also to implement it. It helped me advance in my knowledge. This course helped me defining Deep Learning field, understanding how Deep Learning could potentially impact our business and industry to write a thought leadership piece regarding use cases and industry potential of Machine Learning.

This specialization helped me identifying which aspects of Deep Learning field seem most important and relevant to us, apparently they were all important to us. Walking away with a strong foundation in where Deep Learning is going, what it does, and how to prepare for it.

Deep Learning specialization helped me achieving a good learning and knowledge about that field.

Thank you so much for offering such wonderful piece of art.

Best Regards,

Zeyad

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.

Nesse curso você vai ficar maravilhado com a capacidade científica do ser humano em transformar as redes neurais em máquinas de se comunicar com humanos. As ligações semânticas e contexto das orações e palavras são tornadas inputs matematicamente convertidas em tensores. Para procurar seu correspondente sentido e significado esses tensores são reconvertidos em um processo complexo envolvendo várias etapas e fases. Na saída dos pipelines de algoritmos vc encontrará sentido e significado, que possam traduzir para outra língua ou até mesmo dar respostas e por fim, conversar de forma natural. No mecanismo de atenção você aprenderá como programar seus dispositivos de monitoração de ambientes que possam entregar informação concreta sobre o que está acontecendo.

It was an amazing course. From the beginning to the end, Andrew Ng has laid out all of the parts of the course extremely well. Of course, given the nature of RNNs and their complexity, it will also take your effort to make sure that you understand what he is talking about. Another note about the assignments, previous reviews have mentioned some of the problems and how the previous courses had better structured assignments. I think that the deeplearning.ai team has done a tremendous job of improving the content of this course assignments. At moments, it feels like you are lost, but deep explanations make sure that you understand everything and are able to implements all of the parts of the system that you have to implement. Please take this course!

I like the way Andrew introduce the topic. From the easier cases to the more difficult ones.

It would be better to use @ instead of np.dot. I like it better.

It would be nice to have a simpler method to download the notebooks with all the datasets, images, helper modules. And also to have a description what does one assignment needs to run on my own computer.

Thanks for the possibility to learn deep learning with Python. I am curious whether Julia, that is a kind of mixture of Python and MATLAB with parallel computing, will gain popularity.

As a Linux desktop user the attached pptx files are sometimes hard to read. There is no PPT just LibreOffice on my laptop. I preferred the Machine Learning courses PDF files. But the notebooks are great.

Great topics and discussion, however the lectures started to gloss over the details of implementation which were left entirely to the exercises.

Started to get the basic hang of Tensorflow and Keras by this point in the series, however it was a bit of cut and paste from previous exercises, thus still requiring a lot of forum review to sort out syntax issues.

I get the dynamic aspect of writing the lecture notes in the videos, however the lecture notes should be "cleaned up" in the downloadable files (i.e., typos corrected and typed up). Additionally, the notes written in the video could be written and organized more clearly (e.g., uniform directional flow across the page/screen rather than randomly fit wherever on the page.

I learned what this course says it to be. There are many interesting topics and new ways to do things. This course is quite advanced. If you have been in similar fields you may find a lot of connection, such as, time encoding in the transformer networks. You will learn how to incorporate time axis, time dependency, auto-correlation (you name it) to formulation of a machine learning problem using neural networks. I highly recommend this course if you need a systematic treatment of recent advances in speech recognition, machine translation, natural language processing, and more. The padding and blank words also are interesting way to pre-process or post-process. You will earn a lot of bells and whistles too.

Great introduction to sequence models! Andrew as usual goes into detail on some seminal architectures that have shaped deep learning sequence models over the past ~decade. One feedback: I wish there was a week dedicated to doing just backprop and gradient computations for plain RNNs as well as LSTM-cell based RNNs. The latter is covered to some degree in the programming assignment as an ungraded part, but I don't think that is enough justice to the topic. Also, another feedback: sometimes keeping track of the dimensions of the various entities in play was very difficult for me. Perhaps 10-15 minutes dedicated to explaining just that would be helpful to students. All in all, a fantastic course though!

It was an unbelievable journey through this Deep Learning Specialization! I really felt the power of the tools I obtained during the past 3 weeks that it took me to pass all 5 courses of the specialization. Many of the Programming Assignments are demanding and in the end I could be extremely satisfied that I succeeded in taking them all. Thanks a lot to Andrew Ng and all involved for making this sequence of courses accessible to people like me, and presenting it in such an understandable and interesting way! Now, I can start thinking of the vast potential for using Deep Neural Networks not only in Research and Space Sciences, where my interests are, but also in my daily life. Very many thanks again! AJ

The whole series was excellent but in particular this last course on RNNs. Thank you for not skipping the mathematical details and letting us figure out backpropagations through time and how Adam works under the hood and explaining LSTMs and Attention so well. There was even a notebook on Attention! And the dinosaurus notebook was cool but the jazz improvisation really blew me away: the music actually sounded really nice! :) Also, thank you for pre-training the models to safe us time and teach us how to resume training from learned weights! The quizzes were helpful in developing an intuition and the price point was more than fair. Perfect series, Andrew, thanks a lot!

Best, Maurice

Another excellent course, well presented, with compelling content. My only concerns are regarding the labs. With no previous Python or Keras experience, I found I needed to spend a lot of time coming up to speed on new programming domains in order to complete the assignments (my previous experience is mainly C). While this was somewhat an issue in the previous courses in the Specialization, I found it particularly so in Sequence Models. This distracted from the main objective of understanding the core NN algorithms. I would recommend either: 1) advising students to have a solid background in Python, or 2) a bit more clarity on how to use the Keras functions in the labs.

Previously, I have taken online classes before in Machine Learning by going the cheap route (Udemy, blogs, youtube) and you get what you pay for. Andrew Ng explains it the most thorough, easiest, and simplest way possible. Presentation material is very understandable. Great class for new machine learning learners. Highly recommend it. The only downside is that the programming exercises are little too easy in my opinion. I feel like the best way to get your hands dirty is to do actual projects (do your own projects). These lectures are good for intuition and background of different types of Neural Network architectures. Other than that, Great material. Thanks Andrew!

A very good course. It presented gated units like GRU and LSTM with so much simplicity that anyone can understand it on the first run. The downsides were the Jazz music generation, since it was the only task where the data is non intuitive (MIDI files) so you black-box apply the algorithm to a data you have no idea how it is structured, unless, of course, you are familiar with MIDI files prior to this course. Other than that, the learning curve was a bit slower in the beginning, but explodes by the end of the course, where you put all the subjects you've learned to perform a neural machine translation, which, in my opinion, was hugely awesome and rewarding.

Like all other course in this specialization, this is also indeed a great course. It fundamentally clears concepts and gives very clear concpts for topics such as RNN and LSTM, which can ohterwise can be difficult to digest. Also, the programming excersices, built on great topics, suh as Music synthesis, Trigger word activation, are exciting to work on. The only feedback I would like to suggest, is that topics of Backpropogation for sequence model is critical and should have been taken up indepth in study rather than left to excerciss only. Overall this course is more fast paced and packed 3 weeks which should have been perhaps a 4 week course.

This course teaches in-depth knowledge of sequence models in natural language processing and speech regocnition . The programming excercises and the quizzes provide more content to furthur your grasp on the matter . The progamming exercises being totally in Keras , provides a clear analogy of how LSTM s and GRU s , work along with attention models introduced in the last week. You also have to implement a LSTM and RNN from scratch in Numpy , which provides for the basic knowledge how these architectures actually work. Overall , it was a great experience and taking this course should be a pre-requisite for all learning in NLP.

Whew! This was very interesting and challenging. I have a huge backlog of things I need to go back and read up on and better understand. I really appreciate the work that Andrew and his team put into these courses. The lectures were very well paced and clear. His temperament is exemplary for a teacher and his subject knowledge comes across. I found the exercises really well thought out and beautifully crafted. The coding style could not have been more clear and the consistency made it understandable despite the complexity of the subject and the limited time to delve into the mechanics of Keras and the Python tools. Bravo!

The last course is in this series does not disappoint. I found this course to be more difficult than the others; likely because I had very little prior exposure to recurrent neural networks. However, this course is worth the effort as it opens up a realm of new possibilities; text, audio & time-series data. Whether you need to detect, classify or translate sequences, or even generate new sequences in the vein of some examples, this course is for you. There are several high-level APIs for performing these tasks but having a deeper understanding of what these APIs are doing is invaluable to your success. Take this course.

An extremely well thought off and comprehensive introduction to sequence models, with examples taken from the most important/interesting application domains. Andrew NG's clarity of exposition is absolutely wonderful on such an otherwise complex area. The assignments are very cleverly chosen and helped me to finally get to grips with Keras. This being a new course, the assignment notebooks had a few minor issues that are well known by now and documented in forums and erratas, and will likely be fixed in subsequent reruns. Nevertheless, given the breadth and quality of the content, 5 starts are absolutely well deserved.

This was, in my opinion, the best of the 5 courses. Actually, here's how I'd rank the courses (from best to worse):

5, 1, 2, 3, 4

I learned a lot about sequence models and half-way through the course, I was able to jump right in and try some ideas I had in PyTorch.

The assignments could use a bit more work: I didn't really feel inspired by them and their "fill in the blank" style prevented me from thinking too hard.

All in all, I highly recommend this entire specialization. I was completely clueless about deep learning at the beginning, but now I'm actually trying out some novel ideas!

Thanks so much Andrew and the team.

This course, undoubtedly, has the toughest assignments compared to all the previous courses. The content is rich and informative. Again, pay close attention to the hints given in the programming exercises. If you don't follow, check the Discussion Forums to get a hint. Professor Andrew, your teaching is absolutely sublime - Crisp and concise. Personally, I would have loved an entire week dedicated to Attention Models as the entire concept seemed a bit rushed. Other than that, I have absolutely no qualms! For the people who are enrolling for THIS course only - make sure you're pretty good with Python and Keras.

Such a great course. It explains everything from scratch and teach you how to code in numpy (scratch) and how to code in keras to build high performance system (instead of tiny datasets).

I recommend this corse and the DeepLearning specialization as well. Thank you.

Es un curso muy bueno. En el se explica todo desde cero y te enseña como programar los modelos en Numpy (desde cero) o usando keras para crear modelos de alto rendimiento (a pesar de los datasets pequeños por falta de capacidad de computo).

Recomiendo este curso a todo el mundo asi como tambien las especializacion completa en DeepLearning. Gracias