Introduction
The perceptron
Logistic regression
Implementing text classification using perceptron and logistic regression
Feed-forward neural networks
Best practices in deep learning
Implementing text classification with feed-forward networks
Distributional hypothesis and representation learning
Implementing text classification using word embeddings
Recurrent neural networks
Implementing part-of-speech tagging using recurrent neural networks
Contextualized embeddings and transformer networks
Using transformers with the hugging face library
Encoder-decoder methods
Implementing encoder-decoder methods
Neural architectures for natural language processing applications
Appendices: Overview of the Python language and key libraries ; Character encodings: ASCII and Unicode

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Deep Learning is becoming increasingly important in a technology-dominated world. However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics. This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing. It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way. It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face. Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems.