Named Entity Recognition using LSTMs with Keras

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在此免费的指导 项目中,您将:

Build and train a bi-directional LSTM with Keras

Solve the Named Entity Recognition (NER) problem with LSTMs

在面试中展现此实践经验

Clock1.5 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

必备条件

The prerequisites for the project are prior Python programming experience as well as a theoretical understanding of neural networks.

您要培养的技能

Deep LearningMachine LearningTensorflowLong Short-Term Memory (ISTM)keras

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Project Overview and Import Modules

  2. Load and Explore the NER Dataset

  3. Retrieve Sentences and Corresponding Tags

  4. Define Mappings between Sentences and Tags

  5. Padding Input Sentences and Creating Train/Test Splits

  6. Build and Compile a Bidirectional LSTM Model

  7. Train the Model

  8. Evaluate Named Entity Recognition Model

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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