Introduction of artificial neural network.

1. ARTIFICIAL
NEURAL
NETWORK
- NAINA BHATT

2. CONTENTS
• BIOLOGICAL NEURON MODEL
• ARTIFICIAL NEURAL NETWORK
• TYPES OF ANN
• LEARNING
• APPLICATIONS
• ADVANTAGES
• DISADVANTAGES
• CONCLUSION
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3. BIOLOGICAL NEURON MODEL
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4. 4
• A neuron carries electrical impulses. They are the basic units of the nervous system
and its most important part is the brain.
• Dendrite — It receives signals from other neurons.
• Soma (cell body) — It sums all the incoming signals to generate input.
• Axon — When the sum reaches a threshold value, neuron fires and the signal travels
down the axon to the other neurons.
• Synapses — The point of interconnection of one neuron with other neurons. The
amount of signal transmitted depend upon the strength (synaptic weights) of the
connections.
BIOLOGICAL NEURON MODEL

5. ARTIFICIAL NEURAL NETWORKS
• An artificial neural network (ANN) is a
computational model based on the
structure and functions of biological
neural networks. Information that
flows through the network affects the
structure of the ANN because a neural
network changes - or learns, in a sense
- based on that input and output.
• ANNs are considered nonlinear
statistical data modeling tools where
the complex relationships between
inputs and outputs are modeled or
patterns are found.
• ANN is also known as a neural network.
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• INPUT LAYER — contains those units which
receive input from the outside world on
which network will learn, recognize and
processed.
• OUTPUT LAYER — contains units that respond
to the information about how it’s learned
any task.
• HIDDEN LAYER — These units are in between
input and output layers. The job of hidden
layer is to transform the input into something
that output unit can use in some way.
• In most neural networks , hidden neuron is
fully connected to the every neuron in its
previous layer(input) and to the next layer
(output) layer.
ANN ARCHITECTURE

7. Types of Artificial Neural
Networks
• Multilayer perceptron (MLP)
• Convolutional neural network (CNN)
• Recursive neural network (RNN)
• Long short-term memory (LSTM)
• Recurrent neural network (RNN)
• Sequence-to-sequence models
• Shallow neural networks
• Feedforward Neural Network
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8. • Supervised Learning— The training data is
input to the network, and the desired output
is known weights are adjusted until output
yields desired value.
• Unsupervised Learning— The input data is
used to train the network whose output is
known. The network classifies the input data
and adjusts the weight by feature extraction
in input data.
• Reinforcement Learning— Here the value of
the output is unknown, but the network
provides the feedback whether the output is
right or wrong. It is semi-supervised learning.
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LEARNING

9. APPLICATIONS
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Human Face
Recognition
Ridesharing Apps
Like Uber and Lyft
Handwriting
Recognition
Stock Exchange
Prediction

10.  Storing information on the entire network
 Ability to work with incomplete knowledge
 Having fault tolerance
 Parallel processing capability
 Having a memory distribution
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ADVANTAGES

11. DISADVANTAGES
• Hardware dependence
• Unrecognized behavior of the
network
• The duration of the network is
unknown
• Difficulty of showing the issue to
the network
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12. CONCLUSION
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• Artificial neural networks are inspired by the learning processes that take place in
biological systems.
• Biological neural learning happens by the modification of the synaptic strength.
Artificial neural networks learn in the same way.
• The synapse strength modification rules for artificial neural networks can be derived
by applying mathematical optimisation methods.
• Learning tasks of artificial neural networks can be reformulated as function
approximation tasks.
• Neural networks can be considered as nonlinear function approximating tools (i.e.,
linear combinations of nonlinear basis functions), where the parameters of the
networks should be found by applying optimisation methods.
• The optimisation is done with respect to the approximation error measure.
• In general it is enough to have a single hidden layer neural network (MLP, RBF or
other) to learn the approximation of a nonlinear function. In such cases general
optimisation can be applied to find the change rules for the synaptic weights.

13. ------- NAINA BHATT