Text Classification Using Word2Vec and LSTM on Keras
Learn how to create a Text Classifier using Word Embeddings and LSTM on Tensorflow & Keras.
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.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction & Setting up Google Colab
Loading the Dataset
Preprocessing the Data for Word2Vec
Training the Word2Vec model
Testing the Word2Vec Model
Preparing data for LSTM
Training the LSTM model
Evaluating the LSTM model
Plotting the model performance metrics
Testing the model performance
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In a split-screen video, your instructor guides you step-by-step
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