Machine Learning with TensorFlow

Chapter 6. Hidden Markov models

This chapter covers

• Defining interpretive models
• Using Markov chains to model data
• Inferring hidden state using a hidden Markov model

If a rocket blows up, someone’s probably going to get fired, so rocket scientists and engineers must be able to make confident decisions about all components and configurations. They do so by physical simulations and mathematical deduction from first principles. You, too, have solved science problems with pure logical thinking. Consider Boyle’s law: pressure and volume of a gas are inversely related under a fixed temperature. You can make insightful inferences from these simple ...