Chapter 4: Training a neural network model

In this chapter, you'll learn how to update spaCy's statistical models to customize them for your use case – for example, to predict a new entity type in online comments. You'll write your own training loop from scratch, and understand the basics of how training works, along with tips and tricks that can make your custom NLP projects more successful.

1Training and updating models

2Purpose of training

3Creating training data (1)

4Creating training data (2)

5The training loop

6Setting up the pipeline

7Building a training loop

8Exploring the model

9Training best practices

10Good data vs. bad data

11Training multiple labels

12Wrapping up

About this course

spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive online course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

About me

I'm Ines, one of the core developers of spaCy and the co-founder of Explosion. I specialize in modern developer tools for AI, Machine Learning and NLP. I also really love building stuff for the web.