Brief explanation about the neural network along with its application and current uses.

1. Neural Network
-Ramesh Giri

2. CONTENTS:
• Human Neural Network
• Artificial Neural Network(ANN)
• Applications of ANN
• Neural Networks in software today.
• Perceptron.
• Backpropagation Algorithm

3. WHAT IS NEURAL NETWORK???
• An interconnected web of neurons
transmitting elaborate patterns of
electrical signals
• The human brain can be described as a
biological neural network
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4. ARTIFICIAL NEURAL NETWORK (ANN)
• A computational model based on the structure and functions of
biological neural networks
• A neural network is a “connectionist” computational system
In 1943, Warren S. McCulloch, a neuroscientist, and Walter Pitts, a
logician, developed the first conceptual model of an artificial neural
network
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5. Contd…
• A true neural network does not follow a linear path.
• One of the key elements of a neural network is its ability to learn
• A neural network is not just a complex system, but a
complex adaptive system
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6. APPLICATION OF ANN
• To perform “easy-for-a-human, difficult-for-a-machine” task.
• Applications range from optical character recognition to facial
recognition
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7. PRESENT USES
• Pattern Recognition
• Time Series Prediction
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8. • Signal Processing
• Control
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9. • Soft Sensors
• Anomaly Detection
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10. THE PERCEPTRON
• Invented in 1957 by Frank Rosenblatt at the Cornell Aeronautical
Laboratory.
• The simplest neural network possible
OR
a computational model of a single neuron
• Consists of one or more inputs, a processor, and a single output.
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11. MULTI-LEVEL PERCEPTRON.
• A computational model of a neurons
• Complex to train as there are lots of
processors.
• Outputs generated in same manner as
perceptron.
• Pass through additional layers of neurons before reaching the output
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12. THE BACKPROPAGATION ALGORITHM
• Originally introduced in the 1970s, appreciated in 1976 paper
by David Rumelhart, Geoffrey Hinton, and Ronald Williams.
• Provides us way of computing the derivative ∂C/∂w of the cost
function C with respect to any weight w.
• The learning algorithm specifies the modification of weight.
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13. WORKING
• The input is passed to the input neuron.
• Then the value is passed to a sigmoid function:
𝑌 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 =
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1+𝑒−cx
• The result received at output end is along with error value.
(Y-𝑌∗
), where 𝑌∗
is error.
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14. Contd…
• Error is then detected and corrected using function:
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15. CONCLUSION:
Neural networks are suitable for predicting time series mainly because
of learning only from examples.
Neural networks are able to generalize and are resistant to noise.
On the other hand, it is generally not possible to determine exactly
what a neural network learned and it is also hard to estimate possible
prediction error.
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