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Named Entity Recognition using LSTMs with Keras に戻る

Coursera Project Network による Named Entity Recognition using LSTMs with Keras の受講者のレビューおよびフィードバック



In this 1-hour long project-based course, you will use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....




End to End example of how to implement NLP NER in Keras using bi directional LSTM. Completed notebook can be found in the Coursera project resource page.



Excellent short course with hands on exercise. Wish to do more free courses.


Named Entity Recognition using LSTMs with Keras: 26 - 39 / 39 レビュー

by p s



by Anand S


Really liked the structured approach. Helped me understand the steps involved in building a NER app

by Padala V R


Rhyme interface is too laggy

by Nirmala S


Good learning experience.

by Benjamin B L


Very helpful

by Neelkanth S M


it's too short, plus I expected it to be close to real life projects

by Akshay G


More in-depth explanations on model building and use of libraries could have been useful. I expected a bit more considering I paid USD 10 for this course. And the course felt a bit too short.

by Julie S


I would have been more aware of NER with LSTM by doing a traditional tuto on a random site. There is so few theoric and overall explanation and the voice is monotonous.

by Simon S R


Needs more theoretical explanation alongside, not better than other free tutorials.

by Krishna C M


Nothing useful from this guided course, you can even read a free published article from random source. All the instructor did was read out what he is typing. The ability to code while you watch video, I did not find it much useful.

by Manoj R


It's just the copy of what is available as free in the internet, even the dataset...

by Youyi K


the third party tool... no sound at waste of time and money

by Nisarg A M


At least provide the notebook with code after the course ends.

by Zhiqiu L


All materials are copied directly from somewhere.