neural network

NEURAL NETWORK

PREPARED BY:

NIKITA GARG M.Tech(cs)

NEURAL NETWORK● Neural networks are computational

models .● Neural networks are generally presented

as systems of interconnected "neurons" which can compute values from inputs.

● Neural network help in creating machine with learning capabilities or ability like humans.

CONTINUE● It has a large number of processors

operating in parallel.● Neural networks are sometimes described

in terms of knowledge layers.● It is initially "trained" or fed large amounts

of data and rules about data relationships.

Characteristics of neural network● Neural network exhibit mapping capabilities

they can map input patterns to their associated output patterns.

● Neural network can be learn by examples.● It posses the capability to generalize as

they can predict new outcome from past trends.

CONTINUE●The neural network are robust and fault tolerant.

● They can process information in parallel at high speed and in distributed manner.

ADVANTAGE● ADAPTIVE LEARNING-is an ability to learn

how to do tasks based on the data given for training or initial experience.

● SELF ORGANIZATION-an ANN can create its own organization or representation of the information it receive during learning time.

CONTINUE● REAL TIME OPERATION-ANN

computation may be carried out in parallel ,using special hardware devices designed and manufactured to take advantage of this capability.

● FAULT TOLERANCE-partial destruction of the network lead to a corresponding degradation of performance.

APPLICATION OF NEURAL NETWORK

● Character Recognition – it is used in handheld devices like the Palm Pilot.

● Neural networks can be used to recognize handwritten characters.

● Image Compression - Neural networks can receive and process vast amounts of information at once, making them useful in image compression.

●

CONTINUE● Stock Market Prediction - The day-to-day

business of the stock market is extremely complicated.

● Neural networks can examine a lot of information quickly and sort it all out, they can be used to predict stock prices.

DIFFERENCE BETWEEN ARITIFICAL NEURAL NETWORK AND BIOLOGICAL

NEURAL NETWORK● Artifical neural

network are faster processing information.

● They also learn from past experience to improve their own performance levels.

● Biological neuron are slow in processing information.

● They learn from past experience to improve their own performance levels.

CONTINUE● Artificial neural

networks are based on computational model involving the propagation of continuous variable from one processing unit to the next.

● Biological neural networks communicate through pulses, the timing of the pulses to transmit information and perform computation.

DIAGRAM

CONTINUE● It is stored at the

weights matrix.● Connectivity is

precisely specified.

● It is stored at the synapses.

● Much higher number of connections between neurons.

LEARNING● Learning include complete training.● Learning is categorized into two types:● Supervised learning● Unsupervised learning

SUPERVISED LEARNING● Supervised learning is the machine

learning.● Supervised learning includes two

categories of algorithms:● Classification- for categorical response

values, where the data can be separated into specific “classes”

● Regression- for continuous-response values

SUPERVISED LEARNING

UNSUPERVISED LEARNING● unsupervised learning is that of trying to

find hidden structure in unlabeled data.● Approaches to unsupervised learning

include:● clustering● hidden Markov models,● blind signal separation using feature

extraction techniques for dimensionality reduction.

UNSUPERVISED LEARNING DIAGRAM

COMPONENTS OF ARTIFICAL NEURAL NETWORK

• Learning function- It is to modify the variable connection weights on the inputs of each processing element according to some neural based algorithm.

• This process of changing the weights of the input connections to achieve some desired result can also be called the adaption function.

LEARNING TYPES● Supervised learning- requires a teacher.

The teacher may be a training set of data or an observer who grades the performance of the network results.

● Unsupervised learning- there is no external teacher, the system must organize itself by some internal criteria designed into the network.

● This is learning by doing.

LAYERING● SINGLE-LAYER● The single-layer refers to the output layer.● This layer does not process any

computation over the input values. It just bypasses the input values.

● An example of ANN is a linear assocative memory where an input pattern is associated to an output pattern

CONTINUE● MULTILAYER-NETWORK● It consists of one or more hidden layers of

neurons.● The network is fully connected because

each neuron in one layer is connected to all neurons in the next layer.

DIAGRAM OF MULTILAYER NETWORK

BIOLOGICAL NEURONS

DETAIL ABOUT BIOLOGICAL NEURONS

1. Soma or body cell - is a large, round central body in which almost all the logical functions of the neuron are realized.

2. The axon (output), is a nerve fibre attached to the soma which can serve as a final output channel of the neuron. An axon is usually highly branched.

COND…

1. The dendrites (inputs)- represent a highly branching tree of fibres. These long irregularly shaped nerve fibres (processes) are attached to the soma.

2. Synapses are specialized contacts on a neuron which are the termination points for the axons from other neurons.

ACTIVATION FUNCTION● The activation function of a node defines

the output of that node given an input or set of inputs. 

● The nonlinear activation function that allows networks to compute nontrivial problems using only a small number of nodes.

ACTIVATION FUNCTION

ACTIVATION FUNCTION

Step function Sign function

+1

-1

0

+1

-10X

Y

X

Y

1 1

-1

0 X

Y

Sigmoid function

-1

0 X

Y

Linear function

0if,00if,1

XX

Ystep

0if,10if,1

XX

Ysign Xsigmoid

eY

1

1 XYlinear