Chapter 1. A machine-learning odyssey

This chapter covers

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

Have you ever wondered if there are limits to what computer programs can solve? Nowadays, computers appear to do a lot more than unravel mathematical equations. In the last half-century, programming has become the ultimate tool to automate tasks and save 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 predictions about the stock market perform better than human reasoning? The achievements of the past decade hint that the answer to all these questions is a resounding yes, and the implementations appear to share a common strategy.

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

1.1. Machine-learning fundamentals

1.2. Data representation and features

1.3. Distance metrics

1.4. Types of learning

1.5. TensorFlow

1.6. Overview of future chapters

1.7. Summary

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