1 A machine-learning odyssey

This chapter covers

  • Machine-learning fundamentals
  • Data representation, features, and vector norms
  • Why TensorFlow?

Have you ever wondered whether there are limits to what computer programs can solve? Nowadays, computers appear to do a lot more than unravel mathematical equations. In the past half-century, programming has become the ultimate tool for automating tasks and saving time. But how much can we automate, and how do we go about doing so?

Can a computer observe a photograph and say, “Aha—I see a lovely couple walking over a bridge under an umbrella in the rain”? Can software make medical decisions as accurately as trained professionals can? Can software make better predictions about stock market performance than humans could? The achievements of the past decade hint that the answer to all these questions is a resounding yes and that the implementations appear to have a common strategy.

Recent theoretical advances coupled with newly available technologies have enabled anyone with access to a computer to attempt their own approach to solving these incredibly hard problems. (Okay, not just anyone, but that’s why you’re reading this book, right?)

1.1 Machine-learning fundamentals

1.1.1 Parameters

1.1.2 Learning and inference

1.2 Data representation and features

1.3 Distance metrics

1.4 Types of learning

1.4.1 Supervised learning

1.4.2 Unsupervised learning

1.4.3 Reinforcement learning

1.4.4 Meta-learning

1.5 TensorFlow

1.6 Overview of future chapters

Summary

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