Neural Network
-
Upload
avdhesh-gupta -
Category
Documents
-
view
28 -
download
0
description
Transcript of 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