Artificial neural networks (ANNs) are a machine learning approach modeled after the human brain. ANNs consist of artificial neurons that are connected in a network similar to biological neurons. Each neuron receives inputs, applies an activation function, and outputs a value. ANNs are specified by their neuron model, architecture including connections between neurons with weights, and a learning algorithm to train the network by modifying weights. ANNs have advantages like storing information on the entire network, working with incomplete knowledge, fault tolerance, and parallel processing. However, they also have disadvantages such as hardware dependence, unexplained behavior, difficulty determining network structure, and unknown optimal training duration.

1. Artificial Neural
Network
Business Analytics
PGDM(X) 2021-2022
Ashutosh Mishra
Roll No: 21XPGDM07

2. About ANN
• The first artificial neuron was produced in 1943 by
Warren McCulloch and Walter Pits.
• Artificial neural network (ANN) is a machine
learning approach that models human brain and
consists of a number of artificial neurons.
• Neuron in ANNs tend to have fewer connections
than biological neurons.
• Each neuron in ANN receives a number of inputs.
An activation function is applied to these inputs
which results in activation level of neuron (output
value of the neuron).

3. An Artificial Neural Network is specified
by:
Neuron model: the information processing unit of the NN,
an architecture: a set of neurons and links connecting neurons. Each link has a
weight
a learning algorithm: used for training the NN by modifying the weights in order
to model a particular learning task correctly on the training examples.
The aim is to obtain a NN that is trained and generalizes well. It should behaves
correctly on new instances of the learning task.

4. Advantages
of ANN
• Storing information on the entire network : Information
such as in traditional programming is stored on the entire
network, not on a database.
• Ability to work with incomplete knowledge : After ANN
training, the data may produce output even with
incomplete information. The loss of performance here
depends on the importance of the missing information.
• Having fault tolerance: Corruption of one or more cells of
ANN does not prevent it from generating output. This
feature makes the networks fault tolerant.
• Ability to make machine learning: Artificial neural
networks learn events and make decisions by commenting
on similar events.
• Parallel processing capability: Artificial neural networks
have numerical strength that can perform more than one
job at the same time.

5. Disadvantages
• Hardware dependence: Artificial neural networks require
processors with parallel processing power, in accordance
with their structure. For this reason, the realization of the
equipment is dependent.
• Unexplained behavior of the network: This is the most
important problem of ANN. When ANN produces a probing
solution, it does not give a clue as to why and how. This
reduces trust in the network.
• Determination of proper network structure: There is no
specific rule for determining the structure of artificial
neural networks. Appropriate network structure is
achieved through experience and trial and error.
• The duration of the network is unknown: The network is
reduced to a certain value of the error on the sample
means that the training has been completed. This value
does not give us optimum results.

6. Fig: Layers of artificial neural network