Muddiest Points - Questions

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...

Between Max and Average pooling which is the better of the two and why? In a CNN what is the function of the softmax layer?

Chapter 9 This hidden layer is, in turn, used to calculate a corresponding output, yt. Sequences are processed by presenting one element at a time to the network. The key difference from a feed  forward network lies in the recurrent link shown in the figure with the dashed line. This link augments the input to the hidden layer with the activation value of the hidden layer from the preceding point in time. In the commonly encountered case of soft classification, finding yt consists of a softmax computation that provides a normalized probability distribution over the sequential nature of simple recurrent networks can be illustrated by unrolling the network. For applications that involve much longer input sequences, such as speech recognition, character-by-character sentence processing, or streaming of continuous inputs, unrolling an entire input sequence may not be feasible. In these cases, we can unroll the input into manageable fixed-length segments and treat each segment ...

In Maximum A Posteriori after seeing a particular number of inputs we choose a hypothesis, but as more data come in do we change the hypothesis?