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Questions and Answers
An activation ______ is saturating if $lim_{|v|\to \infty}|\nabla f(v)| = 0$.
An activation ______ is saturating if $lim_{|v|\to \infty}|\nabla f(v)| = 0$.
function
Non-saturating activation functions, such as ReLU, may be better than ______ activation functions.
Non-saturating activation functions, such as ReLU, may be better than ______ activation functions.
saturating
The activation function of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital network of ______ functions.
The activation function of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital network of ______ functions.
activation
The most common activation functions can be divided into three categories: ______ functions, radial functions and fold functions.
The most common activation functions can be divided into three categories: ______ functions, radial functions and fold functions.
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Only ______ activation functions allow such networks to compute nontrivial problems using only a small number of nodes.
Only ______ activation functions allow such networks to compute nontrivial problems using only a small number of nodes.
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Study Notes
Activation Functions
- An activation function is said to be saturating if the limit of the magnitude of its gradient approaches 0 as the input magnitude approaches infinity.
- Non-saturating activation functions, such as ReLU, may be more effective than saturating activation functions.
- The activation function of a node determines the output of that node based on its input or set of inputs.
Digital Networks
- A standard integrated circuit can be viewed as a digital network of activation functions.
Categorization of Activation Functions
- The most common activation functions can be categorized into three types: sigmoid functions, radial functions, and fold functions.
- Only sigmoid activation functions enable networks to compute non-trivial problems using a relatively small number of nodes.
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Description
Test your knowledge of activation functions in artificial neural networks and their importance in achieving nonlinearity. Explore how different activation functions impact the output of nodes in a network and enable complex computations.
