Good

good

Good

GOOD

good

Nice

ok

After taking courses 1&2 of this Specialization I had high expectations for this course on NLP. I am a linguist learning ML so I was really hoping to learn a lot. However, this course has no graded assignments, which was a disappointment as I really enjoy the challenge that those present. Making the assignments not required really takes away from the experience imo. Additionally, the content seems kind of basic in this course. I feel like the first 3 weeks are spent doing mostly the same thing. It isn't until week 4 when we finally get to something somewhat interesting. I really wish this course was better structured. I will be checking out other NLP courses, but this one was a bit of a disappointment.

Very, very basic course. It over-explains the simple things but glosses over important concepts and choices (choice of optimizers, choices of some layers, among others).

Additionally, the course is overly repetitive. Videos explain the same thing over and over. I understand this is more about Python and Tensorflow than is about ML but even then, we spend longer time explaining the non-TF parts of the code than on the TF parts and the reasoning behind them.

This course also lacks practice. Quizzes focus on the exact syntax for a function but not that much on the whys. It lacks programming exercises (first week has a very simple workbook that doesn't teach much).

This course has a lot of exciting material. However, it can be challenging and hard to work on if you are not comfortable with RNNs and LSTMs already. It cross-references to many videos of Andrew Ng, which would be ok, but when you see those videos you realize you need to learn more things and so on. Additionally, the assignment is ungraded which takes away some of the challenges. Definitely a good introduction but to get deeper meaning on this you have to do your own research and studies on the material quite a bit.

Not that bad, but should cover more details. For example, the num_words parameter in Tokenizer is actually len(word_index)+1, but the tutor does not mention that in the lecture. It troubles me a lot in the assignment until I finally figure that out by myself. I still recommend this course If you want to take the tensorflow certificate exam. But you need to learn more by yourself. You'd better read all the API documents for the commands mentioned in this course to make sure that you understand them correctly.

Some lessons in this course were so repetitive that it seemed like a waste of time. Week 2, in particular, felt monotonous and really put a damper on my interest in the information. Despite there being some useful code to learn, Laurence talks though the code in video clips, and then does a screencast of himself talking through the same code in a workbook. I have really enjoyed the 2 courses prior to the NLP course in the TensorFlow in Practice Specialization, but this one seems less developed.

The course material is good.

However, multiple URLs are outdated both in the course material and in coding exercises (which makes some coding exercises not working).

Optimally some of the coding exercises should be updated to newer versions of TensorFlow (some things from the 2.alpha version are no longer available in version 2.4.x and some things are deprecated).

Also, it would be great if the coding exercises were graded (like for earlier courses in this specialization).

The content is good, the videos well paced. The code examples are also very useful.

But I feel the structure of the class is too loose. In my opinion, it would benefit from having assignments that must be submitted and graded.

Maybe they could be small and focused - like focusing on just working with the tokenizer, or setting up Embedding layers or LSTM layers. There could also be one where you load a pretrained model and writing the next token prediction loop.

I'm a bit disappointed with this specialization overall. I think I expected a deeper familiarity with tensorflow, more exposure to the TFData abstraction for large datasets, more low-level exposure to extending your models to fit a specific problem in your domain. Instead I feel like this specialiaztion would better be titled "Black box manipulation of the Keras API". That's a shame, given how solid the first deeplearning.ai specialization was.

This course is out of date and not as polished as the Deep Learning specialization. Data urls in the notebooks are broken. The quizzes are mostly random parameter names that you would google if you needed them, and the week 4 quiz actually has duplicate questions from week 3. The coding exercises are not graded. I did them anyway because I want to learn, but I also want to be challenged and want a certificate that conveys rigor to employers.

This could have been some more intense with 2 quiz in each week (1 or 2 tough questions), giving a written explanation of what a code snippet is meant for or each line of code is meant for, spend time on explaining fundamental concepts. Highlights of course, clear and crisp in explanation of concepts and functioning of code. overall, coherence is well appreciated.

The models developed in the course of the instruction were pretty useless. The instructor didn't discuss enough about how these models could be improved. The content of the course doesn't allow you to actually take on proper NLP and deep learning projects in industry. The demands of the industry are quite different from what's covered in this course

In my opinion, the course was too simple. There are many many concepts that are not covered properly. Even if they recommend going to the deep learning course from Andrew, I believe that at least could explain a bit more some parameters used in the functions and how actually work.

On the other side, you make cool thinks like text generation!

This course does not follow the same standards as the previous courses from deaplearning.ai. The material taught in this course are two basic and do not go in-depth to introduce the major techniques that are being used in the field. The colab notebooks are not provided in most cases and you have to look for them in QA or Github.

In the previous two courses of the specialization, coding exercises were compulsory and graded. In this course, all coding exercises were voluntarily and not well documented. It seemed to me that for whatever reason, the makers of course 3 (natural language processing in tf) put less effort into the making. Bit disappointed.

A good course overall, however the explanations offered on convolutions, LSTMs, GRUs were a bit poor. I know it is beyond the scope of the course, but it will help the student to know what an LSTM is overall and what is the meaning of different arguments (i.e., the `return_sequences` argument in LSTM class).

Basic concepts of NLP. I expect more from this course . Not helpful for real world problem. Should have add more content with more complex and real world problems with programing exercises. No assignments for evaluation of a student understanding. This is not expected from Deeplearning.ai.

It was a good course like the rest in the series, though in this course, they don't link to the colab notebooks that Lawrence works through in the items for each week. The colab notebooks exist on lawrence's colab account but you need to hunt them down. I would suggest fixing this oversight.

This third course provides main NLP concepts using Keras simple example codes. Just like Courses 1 & 2, there's no math and as explained in the videos, if you want a deeper understanding, then you want the "Deep Learning" specialization. Only quizzes, no graded exercises for this course