Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- 1 Introduction
- 2 The Perceptron
- 3 Logistic Regression
- 4 Implementing Text Classification Using Perceptron and Logistic Regression
- 5 Feed-Forward Neural Networks
- 6 Best Practices in Deep Learning
- 7 Implementing Text Classification with Feed-Forward Networks
- 8 Distributional Hypothesis and Representation Learning
- 9 Implementing Text Classification Using Word Embeddings
- 10 Recurrent Neural Networks
- 11 Implementing Part-of-Speech Tagging Using Recurrent Neural Networks
- 12 Contextualized Embeddings and Transformer Networks
- 13 Using Transformers with the Hugging Face Library
- 14 Encoder-Decoder Methods
- 15 Implementing Encoder-Decoder Methods
- 16 Neural Architectures for Natural Language Processing Applications
- Appendix A Overview of the Python Language and Key Libraries
- Appendix B Character Encodings: ASCII and Unicode
- References
- Index
2 - The Perceptron
Published online by Cambridge University Press: 01 February 2024
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- 1 Introduction
- 2 The Perceptron
- 3 Logistic Regression
- 4 Implementing Text Classification Using Perceptron and Logistic Regression
- 5 Feed-Forward Neural Networks
- 6 Best Practices in Deep Learning
- 7 Implementing Text Classification with Feed-Forward Networks
- 8 Distributional Hypothesis and Representation Learning
- 9 Implementing Text Classification Using Word Embeddings
- 10 Recurrent Neural Networks
- 11 Implementing Part-of-Speech Tagging Using Recurrent Neural Networks
- 12 Contextualized Embeddings and Transformer Networks
- 13 Using Transformers with the Hugging Face Library
- 14 Encoder-Decoder Methods
- 15 Implementing Encoder-Decoder Methods
- 16 Neural Architectures for Natural Language Processing Applications
- Appendix A Overview of the Python Language and Key Libraries
- Appendix B Character Encodings: ASCII and Unicode
- References
- Index
Summary
This chapter covers the perceptron, the simplest neural network architecture. In general, neural networks are machine learning architectures loosely inspired by the structure of biological brains. The perceptron is the simplest example of such architectures: it contains a single artificial neuron. The perceptron will form the building block for the more complicated architectures discussed later in the book. However, rather than starting directly with the discussion of this algorithm, we will start with something simpler: a children’s book and some fundamental observations about machine learning. From these, we will formalize our first machine learning algorithm, the perceptron.
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- Information
- Deep Learning for Natural Language ProcessingA Gentle Introduction, pp. 8 - 29Publisher: Cambridge University PressPrint publication year: 2024