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5 Bewertungen

Über den Kurs

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....
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1 - 6 von 6 Bewertungen für Named Entity Recognition using LSTMs with Keras

von Ramprasath A

May 21, 2020

It served the purpose..

von Sylvert I

May 15, 2020

Help me a lot

von Mohamed H G

May 27, 2020

You could increase the time limit for the instructor to explain the functions and logic behind it more elaborately.

And i would be happy if they provided an option for compiling on our own desktops which would be quicker and more efficient on real world Data.

The online GPU was dependent on the internet connectivity and wasn't efficient enough.

On the whole, the course was helpful, Thanks.

von Anantha P

May 25, 2020

Good course covering all the basics required to train a NER model using LSTM without requiring a lot of per-requisite knowledge. Guided project made it easy to follow the instructor and to get an hands on experience

von Ashutosh R

May 28, 2020

Explanations of functions and library used were a little less, otherwise a good course

von Md R U

May 08, 2020

A very short course with very little explanation. Rhyme server keeps restarting without any apparent reason. Probably a YouTube video with a Google Colaboratory workbook would have sufficed. Without exercises to practice on, students can only mindlessly copy what the instructor just did.