Excellent. Isn't Laurence just great! Fantastically deep knowledge, easy learning style, very practical presentation. And funny! A pure joy, highly relevant and extremely useful of course. Thank you!
Great course for anyone interested in NLP! This course focuses on practical learning instead of overburdening students with theory. Would recommend this to every NLP beginner/enthusiast out there!!
创建者 Ajay T•
创建者 Kirt U•
Course material: 5 stars (although it could be more rigorous, this is part of an into to dln with Keras). The course dropped the requirement for code submission which I thought was a bad idea - code submission should be required. Tools: 3 stars - these are standard tools but honestly the tools are still pretty bad (by which I mean you have to use them a bit to get used to them - I have always objected to this is software development and in code I wrote conventional was not relied upon as a requirement).
创建者 James P•
The lectures were great. And I liked that there were still examples for us to work through like the previous courses in the specialization. That being said there were frequently concepts that seemed to be introduced in the examples that were never before mentioned and thus seemed out of place as they were not necessary to complete the assignment. It might be helpful to include short introductory statements to some of these so that we can better learn when/why some of these concepts are used.
创建者 João A J d S•
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.
创建者 Edgar C O•
This a great course on it own, it contains the fundamentals for natural language processing, from the encodings, embeddings and all the process involved before you can actually use the sequences into recurrent neural networks. I was hoping to do more exercises and with a higher difficulty than the ones defined here that are more focussed on the fundamentals. I mean these were good, the pre-processing is always good but I would like more design/program more models.
创建者 Ansgar G•
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.
创建者 Andrei N•
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.
创建者 AbdulSamad M Z•
Gives you a nice overall understanding of what NLP is. There are notebooks to play with concepts. However, this course dials down on the practical aspect (and the theoretical one) even more than the previous course. I think the students will benefit more if more ground is covered on the theoretical aspect of RNNs, LSTMs, and GRUs. Nice course overall.
创建者 Victor A N P•
Like the other courses, this course is very good. It's very hands-on, which is good. However, unlike the previous courses, this course exercises are more like fully completed Colab Notebooks, which we can only run ou change some things. In the previous courses, the notebooks had more exercises, questions and variety. But it's a good course anyway.
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.
创建者 Warren B•
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!
创建者 Parvez M•
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.
创建者 Ashutosh S•
This course should included other Neural methods for NLP to practice in tensorflow and the excercises should be a bit more difficult, they were way too easy to deal with. Assignments help a lot in getting hands on experience. The course overall, gave a nice and concise overview of the tensorflow framework.
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.
创建者 Wouter t B•
Unfortunately the exercises in this course are all ungraded, they don't really have a benchmark goal (in contrast to the earlier courses in the specialization). You're still able to work with 'ungraded' assignments but the difficulty level seems a bit lower.
创建者 Benjamin T•
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.
创建者 Vishal N•
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 :)
创建者 Shaurya K P•
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.
创建者 Ali A•
More info might be provided especially on creating model architecture. I mean in hyperparameter tuning side should be more clarified. What happened when we change emdedding dimension is important to understand whole logic as an example.
创建者 Balaji K•
Extremely interesting field and am super excited to experience the Tensorflow libraries where so much (of code, which I used to write in raw python, years ago !) is encapsulated in simple, ready-to-consume, yet powerful modules.
创建者 Yi S•
At first I though the courses paid too much attention on data preprocessing when implementing NLP.
Well, how to figure out the right way to deal with natural language is what we should learn in this course and it really helps!
创建者 Parth S•
This complete course provides you with a great welcome journey in the world of NLP. Laurence really provided the basics required to understand the topics. Additionally, it was fun to listen to a talk of Andrew & Lawrence.
创建者 家彬 朱•
A good course for NLP, but I like the previous courses more. This course does not deliver the teachings as clear as the previous courses. And I can feel that we've skipped a lot of things in this course.
创建者 Damon W•
These classes are excelling practical examples of how to use tensorflow for various problem types. My only objection is they are slightly light on the actual, behind the scenes, math and intuition.
创建者 Miguel R•
The course is great, but the assignments were not designed as well as the ones in the previous courses. I believe that a careful design of the assignments could significant improve the experience.