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學生對 提供的 Natural Language Processing with Sequence Models 的評價和反饋

890 個評分
178 條評論


In Course 3 of the Natural Language Processing Specialization, you will: a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning. 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....




Overall it was great a course. A little bit weak in theory. I think for practical purposes whatever was sufficient. The detection of Question duplication was a very much cool model. I enjoy it a lot.



This is the third course of NLP Specialization. This was a great course and the instructors was amazing. I really learned and understand everything they thought like LSTM, GRU, Siamese Networks etc.


176 - Natural Language Processing with Sequence Models 的 186 個評論(共 186 個)

創建者 Miguel Á C T


The course is good as an example of code that executes tasks correctly; that is, you can see how neural networks are defined and used in Trax. However, from a pedagogical point of view, I find it quite weak. Concepts are poorly explained and notebooks consist of little more than copying and pasting previously displayed code.

創建者 George L


Compared with the Deep Learning specialization, this specialization was designed in a way that nobody can understand. Although the assignment could be easy at times, the point is being missed when people cannot really understand and learn. Bad teacher. Andrew Ng, please!

創建者 Youran W


All the assignments are extremely similar.

創建者 Xinlong L


I did not enjoy the course at all. It looks like the instructor is just reading materials rather than really teaching. He just focused on reading and did not explain anything. I took Andrew's deep learning specialization, and that course was really great. But I am so disappointed at this course. please do strict quality control on the courses otherwise it harms your brand

創建者 Yanting H


Oversimplified illustration of all core definitions and it is not reasonable from any sense to use trax instead of a popular framework like Tensorflow or Pytorch for the assignment. Also, the design of assignment is weak, you can barely learn anything from filling the blanks.

創建者 Ngacim


1) The course videos just throw out various nouns and you need to goole them to understand what they mean.

2) The assignments try their best to explain concepts in a way that often seems redundant.

創建者 Emanuel D


For me, it is very dissapointing, time is spent on irrelevant things, like python syntax and generators in first week. There are missing video tutorials on how to use Trax.

創建者 Siddharth S


Hard to follow explanations and TRAX absolutely made it super hard to learn and follow.

創建者 Alistair M


Superficial descriptions of the topics; quality definitely lacking

創建者 Alice M


No mentors were available or contactable during this course

創建者 Nicolás E C R


very superficial