Neural Networks

Neural Networks
- Neural Networks can be :
- Biological models
- Artificial models
- Desire to produce artificial systems capable of
sophisticated computations similar to the human brain.
Biological analogy and some main ideas
• The brain is composed of a mass of interconnected neurons
– each neuron is connected to many other neurons
• Neurons transmit signals to each other
• Whether a signal is transmitted is an all-or-nothing event
(the electrical potential in the cell body of the neuron is
• Whether a signal is sent, depends on the strength of the
bond (synapse) between two neurons
How Does the Brain Work ? (1)
- The cell that performs information processing in the brain.
- Fundamental functional unit of all nervous system tissue.
How Does the Brain Work ? (2)
Each consists of :
Brain vs. Digital Computers (1)
- Computers require hundreds of cycles to simulate
a firing of a neuron.
- The brain can fire all the neurons in a single step.
- Serial computers require billions of cycles to
perform some tasks but the brain takes less than
a second.
e.g. Face Recognition
Definition of Neural Network
A Neural Network is a system composed of
many simple processing elements operating in
parallel which can acquire, store, and utilize
experiential knowledge.
Artificial Neural Network?
Neurons vs. Units (1)
• Each element of NN is a node called unit.
• Units are connected by links.
• Each link has a numeric weight.
Neurons vs. units (2)
Real neuron is far away
from our simplified
model - unit
Computing Elements
A typical unit:
Planning in building a Neural Network
Decisions must be taken on the following:
- The number of units to use.
- The type of units required.
- Connection between the units.
How NN learns a task.
Issues to be discussed
- Initializing the weights.
- Use of a learning algorithm.
- Set of training examples.
- Encode the examples as inputs.
- Convert output into meaningful results.
Neural Network Example
A very simple, two-layer, feed-forward network with two inputs, two
hidden nodes, and one output node.
Simple Computations in this network
- There are 2 types of components: Linear and Nonlinear.
- Linear: Input function
- calculate weighted sum of all inputs.
- Non-linear: Activation function
- transform sum into activation level.
Input function:
Activation function g:
A Computing Unit.
Now in more detail but for a particular model only
A unit
Activation Functions
- Use different functions to obtain different models.
- 3 most common choices :
1) Step function
2) Sign function
3) Sigmoid function
- An output of 1 represents firing of a neuron down the
Step Function Perceptrons
3 Activation Functions
Standard structure of an artificial neural
• Input units
– represents the input as a fixed-length vector of numbers (user
• Hidden units
– calculate thresholded weighted sums of the inputs
– represent intermediate calculations that the network learns
• Output units
– represent the output as a fixed length vector of numbers
• Logic rules
– If color = red ^ shape = square then +
• Decision trees
– tree
• Nearest neighbor
– training examples
• Probabilities
– table of probabilities
• Neural networks
– inputs in [0, 1]
Can be used for all of them
Many variants exist
Notation (cont.)
Operation of individual units
• Outputi = f(Wi,j * Inputj + Wi,k * Inputk + Wi,l *
– where f(x) is a threshold (activation) function
– f(x) = 1 / (1 + e-Output)
• “sigmoid”
– f(x) = step function
Artificial Neural Networks
Perceptron Learning Theorem
• Recap: A perceptron (threshold unit) can
learn anything that it can represent (i.e.
anything separable with a hyperplane)
The Exclusive OR problem
A Perceptron cannot represent Exclusive OR
since it is not linearly separable.
Properties of architecture
• No connections within a layer
• No direct connections between input and output layers
• Fully connected between layers
• Often more than 3 layers
• Number of output units need not equal number of input units
• Number of hidden units per layer can be more or less than
input or output units
Each unit is a perceptron
yi 
f (  w ij x j  b i )
Often include bias as an extra weight
Conceptually: Forward Activity Backward Error
Backpropagation learning algorithm ‘BP’
Solution to credit assignment problem in MLP. Rumelhart, Hinton and Williams (1986)
(though actually invented earlier in a PhD thesis relating to economics)
BP has two phases:
Forward pass phase: computes ‘functional signal’, feed forward propagation
of input pattern signals through network
Backward pass phase: computes ‘error signal’, propagates the error backwards
through network starting at output units (where the error is the difference between
actual and desired output values)
Forward Propagation of Activity
• Step 1: Initialize weights at random, choose a
learning rate η
• Until network is trained:
• For each training example i.e. input pattern and
target output(s):
• Step 2: Do forward pass through net (with fixed
weights) to produce output(s)
– i.e., in Forward Direction, layer by layer:
Inputs applied
Multiplied by weights
‘Squashed’ by sigmoid activation function
Output passed to each neuron in next layer
– Repeat above until network output(s) produced
Step 3. Back-propagation of error
• Compute error (delta or local gradient) for each
output unit δ k
• Layer-by-layer, compute error (delta or local
gradient) for each hidden unit δ j by backpropagating
errors (as shown previously)
Step 4: Next, update all the weights Δwij
By gradient descent, and go back to Step 2
 The overall MLP learning algorithm, involving
forward pass and backpropagation of error
(until the network training completion), is
known as the Generalised Delta Rule (GDR),
or more commonly, the Back Propagation
(BP) algorithm
‘Back-prop’ algorithm summary
(with Maths!)
‘Back-prop’ algorithm summary
(with NO Maths!)
MLP/BP: A worked example
Worked example: Forward Pass
Worked example: Forward Pass
Worked example: Backward Pass
Worked example: Update Weights
Using Generalized Delta Rule (BP)
Similarly for the all weights wij:
Verification that it works
• This was a single iteration of back-prop
• Training requires many iterations with many
training examples or epochs (one epoch is entire
presentation of complete training set)
• It can be slow !
• Note that computation in MLP is local (with
respect to each neuron)
• Parallel computation implementation is also
Training and testing data
• How many examples ?
– The more the merrier !
• Disjoint training and testing data sets
– learn from training data but evaluate
performance (generalization ability) on
unseen test data
• Aim: minimize error on test data
More resources
• Binary Logic Unit in an example
• MultiLayer Perceptron Learning Algorithm

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