Named Entity Recognition using LSTMs with Keras
6,576명이 이미 등록했습니다.
6,576명이 이미 등록했습니다.
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)
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.
AS 제공2020년 11월 14일
Really liked the structured approach. Helped me understand the steps involved in building a NER app
SG 제공2020년 9월 5일
This project is a short end-end show. Quickest way to know the process.
BN 제공2020년 5월 29일
Excellent short course with hands on exercise. Wish to do more free courses.
AR 제공2020년 5월 27일
Explanations of functions and library used were a little less, otherwise a good course