# A note about gradient descent: Consider the function f(x)=(x-x 0 ) 2 Its derivative is: - PowerPoint PPT Presentation

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A note about gradient descent: Consider the function f(x)=(x-x 0 ) 2 Its derivative is: By gradient descent. + -. x 0. Solving the differential equation:. or in the general form:. What is the solution of this type of equation:. Try:. THE PERCEPTRON: (Classification).

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A note about gradient descent: Consider the function f(x)=(x-x 0 ) 2 Its derivative is:

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#### Presentation Transcript

Consider the function f(x)=(x-x0)2

Its derivative is:

+ -

x0

Solving the differential equation:

or in the general form:

What is the solution of this type of equation:

Try:

THE PERCEPTRON:

(Classification)

Threshold unit:

where is the output for input pattern ,

are the synaptic weights and is the desired output

AND

w1 w2 w3 w4 w5

1

0 1

-1.5

1 1

AND

Linearly seprable

-0.5

1 1

OR

1

0 1

Linearly separable

Perceptron learning rule:

Convergence proof:

Hertz, Krough, Palmer (HKP)

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Assignment 3a:

program in matlab a preceptron

with a perceptron learning rule

and solve the OR, AND and XOR problems. (Due before Feb 27)

w1 w2 w3 w4 w5

Show Demo

Summary – what can perceptrons do and how?

Linear single layer network:

( approximation, curve fitting)

*

or

Linear unit:

where is the output for input pattern ,

are the synaptic weights and is the desired output

Minimize mean square error:

w1 w2 w3 w4 w5

Linear single layer network:

( approximation, curve fitting)

Linear unit:

where is the output for input pattern ,

are the synaptic weights and is the desired output

Minimize mean square error:

w1 w2 w3 w4 w5

The best solution is obtained when E is minimal.

For linear neurons there is an exact solution for this called the pseudo-inverse (see HKP).

Looking for a solution by gradient descent:

E

w

Chain rule

and

Since:

Error:

Therefore:

Which types of problems can a linear network solve?

Sigmoidal neurons:

for

example:

Which types of problems can a sigmoidal networks solve?

Assignment 3b – Implement a one layer linear and sigmoidal network, fit a 1D a linear, a sigmoid and a quadratic function, for both networks.

Multi layer networks:

Output layer

• Can solve non linearly separable classification problems.

• Can approximate any arbitrary function, given ‘enough’ units in the hidden layer.

Hidden layer

Input layer

Note: is not a vector but a matrix

Solving linearly inseparable problems

XOR

Hint: XOR = or and not and

XOR

-.5

1 0.5

.5

0

0.5 -0.5 1 -1

How do we learn a multi-layer network

The credit assignment problem !

Gradient descent/ Back Propagation, the solution to the credit assignment problem:

Where:

{

From hidden layer to output weights:

For input to hidden layer:

{

Where:

and

and

and

For input to hidden layer:

Assignment 3c: Program a 2 layer network in matlab, solve the XOR problem. Fit the curve: x(x-1) between 0 and 1, how many hidden units did you need?

• Formal neural networks can accomplish many tasks, for example:

• Perform complex classification

• Learn arbitrary functions

• Account for associative memory

• Some applications: Robotics, Character recognition, Speech recognition,

• Medical diagnostics.

• This is not Neuroscience, but is motivated loosely by neuroscience and carries important information for neuroscience as well.

• For example: Memory, learning and some aspects of development are assumed to be based on synaptic plasticity.

What did we learn today?

Is BackProp biologically realistic?