Named Entity Recognition using LSTMs with Keras
Build and train a bi-directional LSTM with Keras
Solve the Named Entity Recognition (NER) problem with LSTMs
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.
Habilidades que desarrollarás
- Deep Learning
- Machine Learning
- Tensorflow
- Long Short-Term Memory (ISTM)
- keras
Aprende paso a paso
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Project Overview and Import Modules
Load and Explore the NER Dataset
Retrieve Sentences and Corresponding Tags
Define Mappings between Sentences and Tags
Padding Input Sentences and Creating Train/Test Splits
Build and Compile a Bidirectional LSTM Model
Train the Model
Evaluate Named Entity Recognition Model
Cómo funcionan los proyectos guiados
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
Reseñas
Principales reseñas sobre NAMED ENTITY RECOGNITION USING LSTMS WITH KERAS
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.
Really liked the structured approach. Helped me understand the steps involved in building a NER app
Explanations of functions and library used were a little less, otherwise a good course
Excellent short course with hands on exercise. Wish to do more free courses.
Preguntas Frecuentes
Preguntas Frecuentes
¿Qué obtendré si compro un proyecto guiado?
¿Hay proyectos guiados disponibles en computadoras de escritorio y dispositivos móviles?
¿Quiénes son los instructores para proyectos guiados?
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¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
¿Puedo participar como oyente de un proyecto guiado y mirar el video de forma gratuita?
¿Cuánta experiencia necesito para hacer este proyecto guiado?
¿Puedo completar este proyecto guiado directamente a través de mi navegador web, en lugar de instalar un software especial?
¿Cómo es la experiencia de aprendizaje con los proyectos guiados?
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