Chapter 2. Fundamental concepts: how do machines learn?

 

Chapter 2 from Grokking Deep Learning by Andrew Trask

In this chapter

  • What are deep learning, machine learning, and artificial intelligence?
  • What are parametric models and nonparametric models?
  • What are supervised learning and unsupervised learning?
  • How can machines learn?

“Machine learning will cause every successful IPO win in five years.”

Eric Schmidt, Google executive chairman, keynote speech, Cloud Computing Platform conference, 2016

What is deep learning?

Deep learning is a subset of methods for machine learning

Deep learning is a subset of machine learning, which is a field dedicated to the study and development of machines that can learn (sometimes with the goal of eventually attaining general artificial intelligence).

In industry, deep learning is used to solve practical tasks in a variety of fields such as computer vision (image), natural language processing (text), and automatic speech recognition (audio). In short, deep learning is a subset of methods in the machine learning toolbox, primarily using artificial neural networks, which are a class of algorithm loosely inspired by the human brain.

What is machine learning?

Supervised machine learning

Unsupervised machine learning

Parametric vs. nonparametric learning

Supervised parametric learning

Unsupervised parametric learning

Nonparametric learning

Summary