Week 10 - Muddiest Point

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

Comments

Post a Comment

Popular posts from this blog

Week 3 - Reading

Chapter 7 7.1 The agent maintains a knowledge base, KB, which may initially contain some background knowledge. Then we can expect it to cross the Golden Gate Bridge because it knows that that will achieve its goal. Notice that this analysis is independent of how the taxi works at the implementation level.  This is called the declarative approach to system building. In contrast, the procedural approach encodes desired behaviors directly as program code. 7.2 The wumpus world is a cave consisting of rooms connected by passageways. Lurking somewhere in the cave is the terrible wumpus, a beast that eats anyone who enters its room. Chapter 8 Propositional logic has a third property that is desirable in representation languages, namely, compositionality.  For nondeductive tasks such as learning from experience, outcomes are necessarily dependent on the form of the representations used.  The language of first-order logic, whose syntax and semantics we define ...
Read more

Week 9 - Reading

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...
Read more

Week 8 - Reading

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 ...
Read more