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Coursera Project Network

Text Classification Using Word2Vec and LSTM on Keras

Coursera Project Network via Coursera

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Overview

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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.

Taught by

Mohammed Murtuza Qureshi

Reviews

5.0 rating, based on 1 Class Central review

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  • Anonymous
    this is best course for learning and i appreciate this course for both academician and developer and beginners and for researcher to study it.

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