Auto Topic: inz
auto_inz | topic
Coverage Score
1
Mentioned Chunks
6
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
inz
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: inz | CO_OCCURS | Propositional Logic | 4 |
| Auto Topic: inz | CO_OCCURS | Constraint Satisfaction Problem | 3 |
| Auto Topic: iny | CO_OCCURS | Auto Topic: inz | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.65 | 6 | : ∂zt ∂wz,z = ∂ ∂wz,z gz(inz,t) = g′ z(inz,t) ∂ ∂wz,z inz,t = g′ z(inz,t) ∂ ∂wz,z ( wz,zzt−1 + wx,zxt + w0,z ) = g′ z(inz,t) ( zt−1 + wz,z ∂zt−1 ∂wz,z ) , (22.15) where the last line uses the rule for derivatives of products: ∂ (uv)/∂x =v∂u/∂x + u∂v/∂x. Looking at Equation (22.15 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | ... y ∂zt ∂wz,z . (22.14) Now the gradient for the hidden unitzt can be obtained from the previous time step as follows: ∂zt ∂wz,z = ∂ ∂wz,z gz(inz,t) = g′ z(inz,t) ∂ ∂wz,z inz,t = g′ z(inz,t) ∂ ∂wz,z ( wz,zzt−1 + wx,zxt + w0,z ) = g′ z(inz,t) ( zt−1 + wz,z ∂zt−1 ∂wz,z ) , (22.15) wh ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... layer indefinitely, copied over (or modified as appropriate) from one time step to the next. Of course, there is a limited amount of storage inz, so it can’t remember everything about all the previous words. In practice RNN models perform well on a variety of tasks, but not on all ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... The equations defining the model refer to the values of the variables indexed by time step t: zt = fw(zt−1,xt) = gz(Wz,zzt−1 + Wx,zxt) ≡ gz(inz,t) ˆyt = gy(Wz,yzt) ≡ gy(iny,t), (22.13) |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... The equations defining the model refer to the values of the variables indexed by time step t: zt = fw(zt−1,xt) = gz(Wz,zzt−1 + Wx,zxt) ≡ gz(inz,t) ˆyt = gy(Wz,yzt) ≡ gy(iny,t), (22.13) Section 22.6 Recurrent Neural Networks 825 where gz and gy denote the activation functions for ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... rn to associate z with x however it chooses. For example, a model trained on images of handwritten digits might choose to use one direction inz space to represent the thickness of pen strokes, another to represent ink color, another to represent background color, and so on. With ... |