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 the following three new tasks covering the three most common use cases of neural networks:

  • 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 be introduced to data preprocessing, basic model architecture principles, and model evaluation.

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

4.2.2 Preparing the data

4.2.3 Building your model

4.2.4 Validating your approach

4.2.5 Generating predictions on new data

4.2.6 A different way to handle the labels and the loss

4.2.7 The importance of having sufficiently large intermediate layers

4.2.8 Further experiments

4.2.9 Wrapping up

4.3 Predicting house prices: A regression example

4.3.1 The Boston housing price dataset

4.3.2 Preparing the data

4.3.3 Building your model

4.3.4 Validating your approach using K-fold validation