4 Getting started with neural networks: Classification and regression

 

This chapter covers

  • Your first examples of real-world machine learning workflows
  • Handling classification problems over vector data
  • Handling continuous regression problems over vector data

This chapter is designed to get you started using neural networks to solve real problems. You’ll consolidate the knowledge you gained from chapters 2 and 3, and you’ll apply what you’ve learned to three new tasks covering the three most common use cases of neural networks—binary classification, multiclass classification, and scalar regression:

  • Classifying movie reviews as positive or negative (binary classification)
  • Classifying news wires by topic (multiclass classification)
  • Estimating the price of a house, given real-estate data (scalar regression)

These examples will be your first contact with end-to-end machine learning workflows: you’ll get introduced to data preprocessing, basic model architecture principles, and model evaluation.

Classification and regression glossary

Classification and regression involve many specialized terms. You’ve come across some of them in earlier examples, and you’ll see more of them in future chapters. They have precise, machine learning–specific definitions, and you should be familiar with them:

4.1 Classifying movie reviews: A binary classification example

4.1.1 The IMDB dataset

4.1.2 Preparing the data

4.1.3 Building your model

4.1.4 Validating your approach

4.1.5 Using a trained model to generate predictions on new data

4.1.6 Further experiments

4.1.7 Wrapping up

4.2 Classifying newswires: A multiclass classification example

4.2.1 The Reuters dataset