Muddiest Points - Questions

Would using a trie data structure benefit us while doing NLP?

Could you please explain bagging and boosting with practical examples, the concept was easy to grasp but would love to see it in a practical sense.

Chapter 2 We’ll begin with the most important tool for describing text patterns: the regular expression. We’ll then turn to a set of tasks collectively called text normalization, in which regular expressions play an important part. One of the unsung successes in standardization in computer science has been the regular expression (RE), a language for specifying text search strings. This language consists of strings with a b, followed by at least two a’s, followed by an exclamation point. The set of operators that allows us to say things like “some number of as” are based on the asterisk or *, commonly called the Kleene. Anchors are special characters that anchor regular expressions to particular places in a string. Since we can’t use the square brackets to search for “cat or dog” (why disjunction can’t we say /[catdog]/?), we need a new operator, the disjunction operator, also called the pipe symbol |. The pattern /cat|dog/ matches either the string cat or the string dog. Thi...

Chapter 21  In the absence of feedback from a teacher, an agent can learn a transition model for its own moves and can perhaps learn to predict the opponent’s moves, but without some feedback about what is good and what is bad, the agent will have no grounds for deciding which move to make. This kind of feedback is called a reward, or reinforcement. Clearly, the passive learning task is similar to the policy evaluation task, part of the policy iteration algorithm described in Section 17.3. The main difference is that the passive learning agent does not know the transition model P(s | s, a), which specifies the probability of reaching state s from state s after doing action a; nor does it know the reward function R(s), which specifies the reward for each state. A simple method for direct utility estimation was invented in the late 1950s in the area of adaptive control theory by Widrow and Hoff (1960). The utility of each state equals its own reward plus the expected utili...