Named Entity Recognition using LSTMs with Keras

4.3
15件の評価
3件のレビュー
提供:
Rhyme
このGuided Projectでは、次のことを行います。

Build and train a bi-directional LSTM with Keras

Solve the Named Entity Recognition (NER) problem with LSTMs

Clock1.5 hours
Intermediate中級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

Deep LearningMachine LearningTensorflowLong Short-Term Memory (ISTM)keras

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  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

How Guided Projects work

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

よくある質問

よくある質問

  • By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

  • Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.

  • Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

  • You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

  • Guided Projects are not eligible for refunds. すべての返金ポリシーを表示する.

  • Financial aid is not available for Guided Projects.

  • Auditing is not available for Guided Projects.

  • At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

  • Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

  • 分割画面環境でタスクをブラウザで直接完了することで学習できます。画面の左側で、ワークスペースでタスクを完了します。画面の右側で、講師がプロジェクトをステップごとにガイドします。

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。