Named Entity Recognition using LSTMs with Keras
4845 ya inscrito
4845 ya inscrito
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.
Long Short-Term Memory (ISTM)
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
por AR27 de may. de 2020
Explanations of functions and library used were a little less, otherwise a good course
por YK18 de jun. de 2021
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.
por MM2 de abr. de 2021
Great course! Gives you a solid understanding of NER.
por BN29 de may. de 2020
Excellent short course with hands on exercise. Wish to do more free courses.