Text Classification Using Word2Vec and LSTM on Keras

提供方
Coursera Project Network
在此指導項目中,您將:

Learn how to create a Text Classifier using Word Embeddings and LSTM on Tensorflow & Keras.

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

In this 2-hour long project-based course, you will learn how to do text classification use pre-trained Word Embeddings and Long Short Term Memory (LSTM) Neural Network using the Deep Learning Framework of Keras and Tensorflow in Python. We will be using Google Colab for writing our code and training the model using the GPU runtime provided by Google on the Notebook. We will first train a Word2Vec model and use its output in the embedding layer of our Deep Learning model LSTM which will then be evaluated for its accuracy and loss on unknown data and tested on few samples. Note: 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.

您要培養的技能

Deep LearningWord2vecPython ProgrammingLong Short-Term Memory (ISTM)keras

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Introduction & Setting up Google Colab

  2. Loading the Dataset

  3. Preprocessing the Data for Word2Vec

  4. Training the Word2Vec model

  5. Testing the Word2Vec Model

  6. Preparing data for LSTM

  7. Training the LSTM model

  8. Evaluating the LSTM model

  9. Plotting the model performance metrics

  10. Testing the model performance

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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