Chevron Left
Volver a Natural Language Processing in TensorFlow

Opiniones y comentarios de aprendices correspondientes a Natural Language Processing in TensorFlow por parte de deeplearning.ai

4.6
estrellas
5,952 calificaciones

Acerca del Curso

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization....

Principales reseñas

GS

26 de ago. de 2019

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!

AS

21 de jul. de 2020

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!!

Filtrar por:

876 - 900 de 934 revisiones para Natural Language Processing in TensorFlow

por Vikas C

24 de dic. de 2019

Good course

por Yining Z

8 de ene. de 2022

too easy

por Hamzeh A

20 de ago. de 2019

good

por Vasileios D S

30 de ago. de 2021

N​ormally the courses of this specialization are well-structured and, although not very demanding, quite complete and self-contained, but in this case the content covered didn't go deep enough and there was very little insight provided into the principle of RNNs and specifically LTSMs, other than pointing to other lectures.

A​lso, while sentiment recognition seemed to be an interesting and promising field, the results of all attempts at text generation were so laughable that it made me wonder as to why was half the course devoted to it instead of some other application area of NLP

por Li P Z

29 de feb. de 2020

Very disappointed in this course. Instructor seems to have limited understanding of how sequence models and word embeddings work, or is unable to communicate the ideas in his teaching. Explanation for the theory is limited, and he has difficulty tying theory to the TensorFlow framework. Not sure why you would begin teaching sequence models with LSTM blocks combined with standard NN, way too complex structure. Instructor doesn't talk about why sequence models are important and useful in the first place. Very very poor.

por Mohamed A S

8 de abr. de 2020

Instead of taking this course, I could've read the tutorials on the TensorFlow site. Those tutorials are regularly updated, maintained, much more detailed and they're FREE.

This course, along the other courses in this specialization are not good for other than exposition to the TF API. Actually, they're not even good at that because the TF tutorials do a much better job at that.

And it's so frustrating that over-fitting is never tackled in any way and not even a hint at how to solve it is even given.

por Sebastian F

9 de ago. de 2019

This was by far the hardest course on the sequence. I actually skip it and did courses on order 1, 2, 4 and now 3.

* Notebooks were not as easy to follow. Maybe put more comments on what was expected and describe the datasets a little more.

* There are typos here and there, for instance "The pervious video referred to a colab environment you can practice one. "-> previous.

The file at https://github.com/tensorflow/datasets/blob/master/docs/datasets.md NOT FOUND

por Axel G

14 de jul. de 2021

Compared to the first two courses of the IBM specialization, this one is made really bad.

They are rushing through the theory. The programming excercises are only ungraded and not very intuitive to solve. You will almost certainly look at the solutions before getting them to run. If you have a look at the forums of the course, there is not much help to find; it looks as if most people cancel the course before they finish.

por Jeff M

22 de jun. de 2021

None of the labs/coding exercises at the end worked and there were numerous other broken links throughout the course. I felt like my skills actually improved in the past two courses, but in this course I just felt like I increased my knowledge. Not a bad thing, but not what I'm looking for.

If all of the courses for the Tensorflow certification were like this, I'd tell folks to avoid the entire program.

por Andrei I

13 de feb. de 2021

The course is merely a walk-through some Jupiter notebooks of Laurence. There are no proper slides with explanation of what's going on. I also don't see much activity from the course creators on the discussion forums. It is incredibly easy to complete the course without forming any deep understanding.

The weekly programming exercises are not even automatically checked for accuracy.

por Jon d

3 de feb. de 2021

I am taking these courses to learn via example. (this is not theory course, it is a course on practice). The fact that there are not well thought out programming exercises makes this course much weaker than the proceeding two. The first two courses in this series are much better for this reason. This course looks unfinished. The lectures are okay, the quizzes are okay.

por Pratik M

5 de jul. de 2020

Very limited practice examples for learners. Also the example are very simple. The course should have been made much detailed and much real example problems. For instance, in the Week 4, topic 'Text Generation', generating a Shakespeare poem seemed to be a very silly example. The quality of Coursera Courses are becoming very poor.

por Aladdin P

5 de ago. de 2020

The material was better in this course than the previous ones, but still lacking depth in my opinion. Also, no graded assignments?? So the focus is then only on the quizzes, and they are not even well done. From week to week the same questions are repeated and the quizzes don't even include code: How is this teaching code?

por Ayesha N

11 de ago. de 2022

Weakest course in this specilaization: the videos were less detailed. I had to really experiment with the labs to actuall see how the data was being converted, and progressed. Also I still dont get the overfitting part. please if you must explain comparisions to your students, do so with the help of graphs and tables

por DAVID R M

4 de oct. de 2020

This course was quite sloppily presented and superficial overall. There were a couple of longstanding errors that have never been fixed (see the lengthy discussions in forums). One thing that annoyed me was that the important concept of stop-words was not discussed at all, yet it was required for the first assignment.

por Tal F

13 de ago. de 2020

All assignments were optional - probably because of all the problems with the scoring system for the previous course. Quizzes often asked things about the dataset we used (eg IMDB) rather than testing that we were learning concepts. Very little meat to the course - mostly links to other resources.

por Fülöp C

18 de abr. de 2021

After completing the Deep Learning specialization, which I really liked, I had high expectations for this one. Unfortunately it can not meet my expectations and was a dissapointment. Even if I try to see it objectively and ignore my high expectations, the quality of the exercises were very poor.

por Hartger

29 de sep. de 2020

Overall the video material is fine. The assignments however are very unclear and contain bugs. The grader's test don't match the instructions. It's very frustrating that the assignments clearly haven't been given the same attention the rest of the course has been.

por Prosenjit D

16 de ene. de 2020

This course is a far cry from Andrew Ng's deep learning specialization and refers to Sequence Models from that specialization at the drop of a hat. In short, no use doing this one, unless you have done sequence models (course 5) of deep learning specialization.

por Dominik B

10 de jun. de 2020

No grader exercises,

sample code in the lectures isn't always updated and gives errors,

everything is a bit chaotic (eg order of sample code, sample code description, introduction to the topic is random; some random parts in the code).

por Venkata S Y T

4 de abr. de 2020

The weekly exercises are not graded and the over all content quality of this course in comparison with the previous two in the specialization seems a bit poor and doesn't provide more learning on the topic.

por Devita P M M

14 de abr. de 2022

cant fix kernel died, padahal sudah ke pusat bantuan dan forum diskusi tetap tidak bisa, alhasil kurang maksimal dalam mengerjakan assigment karena salah satu poinnya tidak bisa dirun, terimakasih

por Amit K

25 de may. de 2020

Not clearly explained and only using toy and irrelevant datasets, nothing realtime industry specific examples. Also, voice quality is very bad for this course.

por Jurica S

29 de nov. de 2019

I would call this entry/beginner level material. There arent any graded coding challenges, which is a shame. No complex topics are covered with this class.

por Jack C

26 de may. de 2020

It's a bit too basic and there are not many graded examples to work through like Andrew Ng's course. I feel it could have been more complete and in depth