Great fun!

Excellent

Thank you

very good

Excellent

Thank You

Awesome!

Thanks!

elegant

Amazing

great!

Great!

good

GJ

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I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

The explanations in the videos are good. And you get a fast intro into NLP with Tensorflow (Keras) with good, working code examples. However, due to the shortness of the course, it lacks quite some depth. The biggest disadvantage in my view is that often the programming exercises are not graded. This course is intended to give you practical skills. Then, the programming needs to be graded and cannot be optional.

Very detailed step by step tutorials of using Tensorflow with lots of effort to make things as easy to understand as possible. The use cases also quite interesting. A little lack of theory comparing to other courses by deeplearning.ai. Quizzes are quite undeveloped. But that is understandable, because the main goal of the course to introduce Tensoflow.

This course is very interesting BUT there is no responsible person in the discussion to answer people who ask. (that's why I give only 4 stars)

It's good to add some resume after the course about the name of function and argument end things like that, this will help people who hate to return always to the documentation always.

And thank you.

This course provided a nice survey of some of the NLP techniques that can be brought to bear to make sense of language. It was a nice touch that we got a peek at one way that one might produce language (reversing some of the techniques to make sense of language).

While not state of the art, this is a good intro into the field!

A fantastic way of explaining things. Used a number of datasets to introduced different situations. However, it contains some drawbacks. For example, maybe the notebook is written using old API, hence the data are needed to be wrapped using `np.array()`. Again, It would be better if the notebooks are graded too.

Enjoyed the course, more content that the other lessons in the series. Still lacks notes and direct codes to save and practice on our own rather using the google colab that could be in the future require subscription. Good explanations can't wait to start the last course on the series.

More intuition for different choices of hyperparameters (layer types, layer specifications) would have been great.

Named Entity Recognition is one of the most important NLP tasks in the Industry, but it is completely missing.

Transformers are missing.

I'm not as satisfied with this course as I am with CNN or Intro to TensorFlow, main reason being there was no graded exercise materials unlike the other two above mentioned ones. I still loved the videos nonetheless. Thanks Laurence and Andrew :)

I'm missing the programming assignments as in earlier courses also i also felt a lack in links of google notebook and we only have videos of the programs working rather than getting hands on with links to corresponding google colab notebooks.