One-Hot Encoding vs Word Embedding

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Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric.

This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model.

The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods.

In this tutorial, you will discover how to encode categorical data when developing neural network models in Keras.

After completing this tutorial, you will know:

(1) The challenge of working with categorical data when using machine learning and deep learning models.

(2) How to integer encode and one hot encode categorical variables for modeling.

(3) How to learn an embedding distributed representation as part of a neural network for categorical variables.

https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/

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What is One Hot Encoding?

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Machine learning algorithms cannot work with categorical data directly.

Categorical data must be converted to numbers.

In this tutorial, you will discover how to convert your input or output sequence data to a one hot encoding for use in sequence classification problems with deep learning in Python.

After completing this tutorial, you will know:

(1) What an integer encoding and one hot encoding are and why they are necessary in machine learning.

(2) How to calculate an integer encoding and one hot encoding by hand in Python.

(3) How to use the scikit-learn and Keras libraries to automatically encode your sequence data in Python.

https://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/

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