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學生對 deeplearning.ai 提供的 Natural Language Processing with Probabilistic Models 的評價和反饋

4.7
1,372 個評分

課程概述

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....

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NM

2020年12月12日

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

2020年12月2日

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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51 - Natural Language Processing with Probabilistic Models 的 75 個評論(共 242 個)

創建者 Harshavardhan S

2020年12月3日

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!!

創建者 Sazzadur R

2021年8月4日

Another great course introducing the probabilistic modelling concepts and slowly getting to the direction of computing neural networks. One must learn in detail how embedding works.

創建者 Aniruddha S H

2020年9月29日

Very good course! helped me clearly learn about Autocorrect, edit distance, Markov chains, n grams, perplexity, backoff, interpolation, word embeddings, CBOW. This was very helpful!

創建者 Bharathi k N

2020年9月11日

This is one of the best courses i have taken. I have learned a lot from this course. Assignments were great and challenging. Thank you deeplearning.ai team for this amazing course.

創建者 Aditya h

2020年10月9日

Thoroughly relished this course. Each and every concept is explained in depth as well as there is a companion notebook to explain as well as practically implement the concepts.

創建者 Kazuomi K

2020年7月1日

This course is very good introduction to NLP Probabilistic models such as Hidden Markov model, N-Gram Language model, and Word2Vec with Python programming assignments.

創建者 Marc G

2022年1月24日

Excellent course! Well designed and taught. I would have liked more advices on how to preprocess text before applying word embeddings (lemmatization, stemming, etc.)

創建者 Veronica B

2022年7月29日

T​his course was really great. Most videos have small understanding questions at the end. The final assignment was the peak of the smaller lecture notebooks.

創建者 Jian G

2020年10月27日

this course is well-designed. It incorporates all factors that make a successful online course. bitesize video, easy to understand, exercise notebooks, etc.

創建者 bob n

2021年2月13日

Nicely broken into digestible chunks. Labs well done, not too easy, and too too frustrating. Material presented clearly and in (again) nice small steps.

創建者 ps

2021年5月30日

I'm really thankful to the professors for sharing there knowledge and experience and creating this excellent course. I have learnt a a lot. Thank You !!!

創建者 Abanoub Y

2020年12月28日

A great course in the very spirit of the original Andrew Ng's ML course with lots of details and explanations of fundamental approaches and techniques.

創建者 Ivan V S

2021年9月26日

I​ grade 5 stars, but take in account, that this course is very specific. It provides real basics of NN and NLP and it is more fundamental than apply.

創建者 Baurjan S

2020年8月29日

Totally enjoyed it. I took a Deep Learning course a couple of years ago and in some respect, it was a great refreshment form two years ago. Thank you!

創建者 Aanand

2021年2月3日

Course well structured. SBOW very well explained and registered firmly. Word embeddings explained very well. Overall very happy from the learning’s

創建者 Long H T

2021年6月14日

This course is amazing! I could not know that I can learn so many interesting things! I am so happy to take the next course in the specialization.

創建者 Alex M

2021年1月7日

Es extenso, pero super interesante la forma de aprender por coursera, cbow model es super chevere. aprendí también, temas de toquenizar textos.

創建者 Ankur G

2020年9月26日

More fun if it would have more ungraded coding problem to solve ,It would be optional so that who wants to do more practice can be benefitted.

創建者 Hieu D T

2021年4月21日

Very well built lectures. The content is foundational enough for new student like me. I feel more comfortable with keywords in this field now.

創建者 Russell H

2020年8月18日

A bit light on the math vs. some other ML courses I have taken, but the good news is that this lets the focus be on the NLP-specific material.

創建者 Kartik S

2020年9月6日

The course content was really engaging. This really helped in understanding many of the basic foundational models for pivotal tasks of NLP.

創建者 Prakhar M

2020年11月10日

Very intresting and effective way of studing NLP . Totally amazing and 10/10 for the clearity of lecture delivery and video presentation .

創建者 Andrés F R P

2021年11月16日

Excelente! Me gustó mucho como enseñan la intuición y matemática detrás de los modelos de lenguaje probabilisticos y Word Embeddings.

創建者 Anshul B

2021年3月7日

I liked this better than the 1st course in the specialization. Instructors cover some real fundamental concepts and techniques in NLP

創建者 Vedika P

2020年10月19日

Brilliant course. Very enriching content and so very well explained. Challenging assignments made me explore each concept in-depth.