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Bewertung und Feedback des Lernenden für Natural Language Processing in TensorFlow von deeplearning.ai

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920 Bewertungen

Über den Kurs

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....

Top-Bewertungen

GS

26. Aug. 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. Juli 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!!

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851 - 875 von 924 Bewertungen für Natural Language Processing in TensorFlow

von Swetha S

10. Mai 2020

No graded assignments. No conceptual explanation.

von Biao W

16. Apr. 2020

Need more explanations on the RNN models itself.

von Soumya B

21. Nov. 2021

Please add Coursera assignment based on coding

von Ramil A

15. Apr. 2020

I wish there were more graded projects.

von Igors K

21. Nov. 2019

No practical exercises that one must do

von Shubham A G

31. Aug. 2019

A bit too easy and no real assignments

von Yuxuan C

12. Apr. 2020

I wish there were graded assignments.

von Ashwin H

25. Apr. 2020

Coding assignments are much needed!

von Ahmad O

14. Sep. 2020

Assignments need some improvment.

von SUMIT V

28. Mai 2020

not enough programming exercises

von giuseppe d

19. Juli 2020

Concepts explained too quiclky

von Salem S

16. Apr. 2020

Code should be explained more

von albert

20. Juni 2020

Not challenging enough....

von Ankit G

17. Mai 2020

No programming assignments

von Leon V

13. Juni 2020

Force me to write code.

von Artem K

6. Okt. 2020

Need more practice

von Vikas C

24. Dez. 2019

Good course

von Yining Z

8. Jan. 2022

too easy

von Hamzeh A

20. Aug. 2019

good

von Vasileios D S

30. Aug. 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

von Li P Z

29. Feb. 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.

von Mohamed A S

8. Apr. 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.

von Sebastian F

9. Aug. 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

von Axel G

14. Juli 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.

von Jeff M

22. Juni 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.