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  • 4 - Implementing Text Classification Using Perceptron and Logistic Regression

  • Mihai Surdeanu, University of Arizona, Marco Antonio Valenzuela-Escárcega, University of Arizona
  • Book:
    Deep Learning for Natural Language Processing
    Published online:
    01 February 2024
    Print publication:
    08 February 2024, pp 49-72

  • Summary

    In the previous chapters, we have discussed the theory behind the perceptron and logistic regression, including mathematical explanations of how and why they are able to learn from examples. In this chapter, we will transition from math to code. Specifically, we will discuss how to implement these models in the Python programming language. All the code that we will introduce throughout this book is available in this GitHub repository as well: https://github.com/clulab/gentlenlp. To get a better understanding of how these algorithms work under the hood, we will start by implementing them from scratch. However, as the book progresses, we will introduce some of the popular tools and libraries that make Python the language of choice for machine learning – for example, PyTorch, and Hugging Face’s transformers. The code for all the examples in the book is provided in the form of Jupyter notebooks. Fragments of these notebooks will be presented in the implementation chapters so that the reader has the whole picture just by reading the book.