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Bewertung und Feedback des Lernenden für Natural Language Processing with Probabilistic Models von deeplearning.ai

4.7
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1,342 Bewertungen
237 Bewertungen

Über den Kurs

In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper....

Top-Bewertungen

NM

12. Dez. 2020

A truly great course, focuses on the details you need, at a good pace, building up the foundations needed before relying more heavily on libraries an abstractions (which I assume will follow).

HS

2. Dez. 2020

A neatly organized course introducing the students to basics of Processing text data, learning word embedding and most importantly on how to interpret the word embedding. Great Job!!

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201 - 225 von 237 Bewertungen für Natural Language Processing with Probabilistic Models

von Ramprakash V

19. Aug. 2020

The course is exceptional in its own way by bringing people to the understanding of probabilistic models. Crisp & Clear. But one need to explore & practise more to gain expertise.

von Cheng J

9. Sep. 2020

The Viterbi algorithm introduction is a bit hard for us to follow. Probably some writings may help guiding through each steps.

von Hernan J

4. Nov. 2020

Esta especialización junto con la de Deep Learning se complementan y es son más claros los conceptos y prácticas, gracias!

von Daniel W

25. März 2021

The tutor sometimes pass the slices too swiftly. I hope that he could wait 2-3 seconds after finishing speaking.

von Sandeep V

2. Okt. 2020

Sone Quiz should also be there. Assignments can be solved by python knowledge an following the instruction

von Gopal M

5. Sep. 2020

Assignments were incorrect.

Lot of content was squeezed in the last week. Even spread would be ideal

von Aung Z P

14. Juli 2020

I love the way the instructor teach and the course design which is made to be simple but effective

von John F

19. Feb. 2022

great material and presentation, there are a few typos here and there but not a big deal

von Mounir H

1. Dez. 2020

I didn't like weeks 1 and 2 too much but I liked week 3 and I really liked week 4.

von bdug

5. Apr. 2021

I liked the lecture, very well prepared. Only the part on metrics was a bit short

von Vladimir V

20. Juli 2020

This is a good course but I would like to see more emphasis on the mathematics.

von Manuel V B

11. Apr. 2021

Great course, but the last week felt a bit messy with submission evaluation.

von Sophie Z

3. Jan. 2021

Not sure if it is on purpose, but W4 labs have repeating content.

von Trần T V

4. Dez. 2021

wonderful course, I learned the fundamental of NLP. Many thanks

von Jinsong T

8. Juni 2021

T​oo basic and going at too slow a pace

von Esakki p E m

11. Apr. 2021

excellent Material & teaching

von AVIJIT J

16. Aug. 2021

G​ood, very good.

von Randall K

14. Apr. 2021

great course

von ugur b

2. Jan. 2022

Veri good

von MoChuxian

31. Okt. 2020

great

von Teresa M B

5. Sep. 2021

good:

* some of the content is well-explained

* provides good solid knowledge about the background and implementation of common NLP tasks

less good:

* notebooks (and content generally) are unevenly distributed

* significantly stronger focus on ML, rather than on the NL side (this is consistent throughout the specialization)

* some of the explanations (e.g. in week 2) aren't clear

* specialization could be structured better -- word embeddings are introduced in course 1, but the in-depth discussion is here in week 4; would perhaps have made more sense to have that content build on itself

von J N B P

5. März 2021

In this course, you will learn to build an autocorrect model and different methods of building this model. The course felt a bit rushed with a lack of detailed explanation, students who are familiar with the concepts of NLP from before starting this specialization won't face any problem, but students who had just begun learning NLP through this specialization might feel a little difficult.

von Amlan C

17. Sep. 2020

Too many gaps in the course. Many concepts not covered in the mathematical sense basic Grad. Desc. math would have been helpful. Also if you want to omit it totally you should have atleast one lab on how one would do it in real life using which library? Pytorch? Keras? What? Rest of the course is okay. Younous is great in explanation.

von Gent S

8. Apr. 2021

The course material is good and you can learn new things, you can exercise python skills a lot as the assignments are quite long. However, the tutors are not the best in explaining the material as well as the videos are a bit vague. It would have helped if the tutors were a bit more experienced in teaching, but still overall good!

von Aditya J

14. Aug. 2020

well I did deep learning specialization earlier things are mathematical, but here they don't go much into maths, and please make some concept chart, to link different algorithms.