Our world is rich with natural language data. Over the past several decades, the way we communicate with one another has shifted to the digital realm and, as such, this data can be used to build models that can improve our online experience. From returning relevant results within a search engine, to autocompleting the next word you type in an email, the benefits of being able to extract insights from natural language is clear to see.

While the way we, as humans, understand language differs notably from the way a model or artificial intelligence may understand it, by shedding light on machine learning and what it is used for, we can begin to understand just how these deep learning models understand language and what fundamentally happens when a model learns from data.

Throughout this book, we will explore this application of artificial intelligence and deep learning to natural language. Through the use of PyTorch,...

Fundamentally, machine learning is the algorithmic process used to identify patterns and extract trends from data. By training specific machine learning algorithms on data, a machine learning model may learn insights that aren't immediately obvious to the human eye. A medical imaging model may learn to detect cancer from images of the human body, while a sentiment analysis model may learn that a book review containing the words good, excellent, and entertaining is more likely to be a positive review than one containing the words bad, terrible, and boring.

Broadly speaking, machine learning algorithms fall into two main categories: supervised learning and unsupervised learning.

Supervised learning

Supervised learning covers any task where we wish to use an input to predict an output. Let's say we wish to train a model to predict house prices. We know that larger houses tend to sell for more money, but we don't know the exact...