Neural Networks: Representation

Non-linear hypotheses

Neural Networks.

Neurons and the brain

Origins: Algorithms that try to mimic the brain.
Was very widely used in 80s and early 90s; popularity diminished in late 90s.
Recent resurgence: State-of-the-art technique for many applications.

the 'one learning algorithm' hypothesis

Sensor representations in the brain.

Model representation I

include

• Input layer
• Hidden layer (Not only one)
• Ouput layer

= "activation" of unit in layer
= matrix of weights controling function mapping from layer to layer

If network have units in layer , units in layer , then will be of dimension

Examples and intuitions I

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Multi-class classification

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