MACHINE LEARNING LECTURE NOTES
UNIT – III
Supervised Learning – II (Neural Networks)
Neural Network Representation – Problems – Perceptrons , Activation Functions, Artificial Neural
Networks (ANN) , Back Propagation Algorithm.
Convolutional Neural Networks - Convolution and Pooling layers, , Recurrent Neural Networks
(RNN).
Classification Metrics: Confusion matrix, Precision, Recall, Accuracy, F-Score, ROC curves
Artificial Neural Network
Artificial Neural Network Tutorial provides basic and advanced concepts of ANNs. Our Artificial
Neural Network tutorial is developed for beginners as well as professions.
The term "Artificial neural network" refers to a biologically inspired sub-field of artificial intelligence
modeled after the brain. An Artificial neural network is usually a computational network based on
biological neural networks that construct the structure of the human brain. Similar to a human brain
has neurons interconnected to each other, artificial neural networks also have neurons that are linked to
each other in various layers of the networks. These neurons are known as nodes.
Artificial neural network tutorial covers all the aspects related to the artificial neural network. In this
tutorial, we will discuss ANNs, Adaptive resonance theory, Kohonen self-organizing map, Building
blocks, unsupervised learning, Genetic algorithm, etc.
What is Artificial Neural Network?
The term "Artificial Neural Network" is derived from Biological neural networks that develop the
structure of a human brain. Similar to the human brain that has neurons interconnected to one another,
artificial neural networks also have neurons that are interconnected to one another in various layers of
the networks. These neurons are known as nodes.
BY
B SARITHA
1
,MACHINE LEARNING LECTURE NOTES
The given figure illustrates the typical diagram of Biological Neural Network.
The typical Artificial Neural Network looks something like the given figure.
Dendrites from Biological Neural Network represent inputs in Artificial Neural Networks, cell nucleus
represents Nodes, synapse represents Weights, and Axon represents Output.
Relationship between Biological neural network and artificial neural network:
Biological Neural Network Artificial Neural Network
Dendrites Inputs
Cell nucleus Nodes
Synapse Weights
Axon Output
BY
B SARITHA
2
,MACHINE LEARNING LECTURE NOTES
An Artificial Neural Network in the field of Artificial intelligence where it attempts to mimic the
network of neurons makes up a human brain so that computers will have an option to understand
things and make decisions in a human-like manner. The artificial neural network is designed by
programming computers to behave simply like interconnected brain cells.
There are around 1000 billion neurons in the human brain. Each neuron has an association point
somewhere in the range of 1,000 and 100,000. In the human brain, data is stored in such a manner as to
be distributed, and we can extract more than one piece of this data when necessary from our memory
parallelly. We can say that the human brain is made up of incredibly amazing parallel processors.
We can understand the artificial neural network with an example, consider an example of a digital
logic gate that takes an input and gives an output. "OR" gate, which takes two inputs. If one or both the
inputs are "On," then we get "On" in output. If both the inputs are "Off," then we get "Off" in output.
Here the output depends upon input. Our brain does not perform the same task. The outputs to inputs
relationship keep changing because of the neurons in our brain, which are "learning."
The architecture of an artificial neural network:
To understand the concept of the architecture of an artificial neural network, we have to understand
what a neural network consists of. In order to define a neural network that consists of a large number
of artificial neurons, which are termed units arranged in a sequence of layers. Lets us look at various
types of layers available in an artificial neural network.
Artificial Neural Network primarily consists of three layers:
Input Layer:
As the name suggests, it accepts inputs in several different formats provided by the programmer.
Hidden Layer:
The hidden layer presents in-between input and output layers. It performs all the calculations to find
hidden features and patterns.
BY
B SARITHA
3
, MACHINE LEARNING LECTURE NOTES
Output Layer:
The input goes through a series of transformations using the hidden layer, which finally results in
output that is conveyed using this layer.
The artificial neural network takes input and computes the weighted sum of the inputs and includes a
bias. This computation is represented in the form of a transfer function.
It determines weighted total is passed as an input to an activation function to produce the output.
Activation functions choose whether a node should fire or not. Only those who are fired make it to the
output layer. There are distinctive activation functions available that can be applied upon the sort of
task we are performing.
Advantages of Artificial Neural Network (ANN)
Parallel processing capability:
Artificial neural networks have a numerical value that can perform more than one task simultaneously.
Storing data on the entire network:
Data that is used in traditional programming is stored on the whole network, not on a database. The
disappearance of a couple of pieces of data in one place doesn't prevent the network from working.
Capability to work with incomplete knowledge:
After ANN training, the information may produce output even with inadequate data. The loss of
performance here relies upon the significance of missing data.
Having a memory distribution:
For ANN is to be able to adapt, it is important to determine the examples and to encourage the network
according to the desired output by demonstrating these examples to the network. The succession of the
network is directly proportional to the chosen instances, and if the event can't appear to the network in
all its aspects, it can produce false output.
Having fault tolerance:
Extortion of one or more cells of ANN does not prohibit it from generating output, and this feature
makes the network fault-tolerance.
BY
B SARITHA
4
UNIT – III
Supervised Learning – II (Neural Networks)
Neural Network Representation – Problems – Perceptrons , Activation Functions, Artificial Neural
Networks (ANN) , Back Propagation Algorithm.
Convolutional Neural Networks - Convolution and Pooling layers, , Recurrent Neural Networks
(RNN).
Classification Metrics: Confusion matrix, Precision, Recall, Accuracy, F-Score, ROC curves
Artificial Neural Network
Artificial Neural Network Tutorial provides basic and advanced concepts of ANNs. Our Artificial
Neural Network tutorial is developed for beginners as well as professions.
The term "Artificial neural network" refers to a biologically inspired sub-field of artificial intelligence
modeled after the brain. An Artificial neural network is usually a computational network based on
biological neural networks that construct the structure of the human brain. Similar to a human brain
has neurons interconnected to each other, artificial neural networks also have neurons that are linked to
each other in various layers of the networks. These neurons are known as nodes.
Artificial neural network tutorial covers all the aspects related to the artificial neural network. In this
tutorial, we will discuss ANNs, Adaptive resonance theory, Kohonen self-organizing map, Building
blocks, unsupervised learning, Genetic algorithm, etc.
What is Artificial Neural Network?
The term "Artificial Neural Network" is derived from Biological neural networks that develop the
structure of a human brain. Similar to the human brain that has neurons interconnected to one another,
artificial neural networks also have neurons that are interconnected to one another in various layers of
the networks. These neurons are known as nodes.
BY
B SARITHA
1
,MACHINE LEARNING LECTURE NOTES
The given figure illustrates the typical diagram of Biological Neural Network.
The typical Artificial Neural Network looks something like the given figure.
Dendrites from Biological Neural Network represent inputs in Artificial Neural Networks, cell nucleus
represents Nodes, synapse represents Weights, and Axon represents Output.
Relationship between Biological neural network and artificial neural network:
Biological Neural Network Artificial Neural Network
Dendrites Inputs
Cell nucleus Nodes
Synapse Weights
Axon Output
BY
B SARITHA
2
,MACHINE LEARNING LECTURE NOTES
An Artificial Neural Network in the field of Artificial intelligence where it attempts to mimic the
network of neurons makes up a human brain so that computers will have an option to understand
things and make decisions in a human-like manner. The artificial neural network is designed by
programming computers to behave simply like interconnected brain cells.
There are around 1000 billion neurons in the human brain. Each neuron has an association point
somewhere in the range of 1,000 and 100,000. In the human brain, data is stored in such a manner as to
be distributed, and we can extract more than one piece of this data when necessary from our memory
parallelly. We can say that the human brain is made up of incredibly amazing parallel processors.
We can understand the artificial neural network with an example, consider an example of a digital
logic gate that takes an input and gives an output. "OR" gate, which takes two inputs. If one or both the
inputs are "On," then we get "On" in output. If both the inputs are "Off," then we get "Off" in output.
Here the output depends upon input. Our brain does not perform the same task. The outputs to inputs
relationship keep changing because of the neurons in our brain, which are "learning."
The architecture of an artificial neural network:
To understand the concept of the architecture of an artificial neural network, we have to understand
what a neural network consists of. In order to define a neural network that consists of a large number
of artificial neurons, which are termed units arranged in a sequence of layers. Lets us look at various
types of layers available in an artificial neural network.
Artificial Neural Network primarily consists of three layers:
Input Layer:
As the name suggests, it accepts inputs in several different formats provided by the programmer.
Hidden Layer:
The hidden layer presents in-between input and output layers. It performs all the calculations to find
hidden features and patterns.
BY
B SARITHA
3
, MACHINE LEARNING LECTURE NOTES
Output Layer:
The input goes through a series of transformations using the hidden layer, which finally results in
output that is conveyed using this layer.
The artificial neural network takes input and computes the weighted sum of the inputs and includes a
bias. This computation is represented in the form of a transfer function.
It determines weighted total is passed as an input to an activation function to produce the output.
Activation functions choose whether a node should fire or not. Only those who are fired make it to the
output layer. There are distinctive activation functions available that can be applied upon the sort of
task we are performing.
Advantages of Artificial Neural Network (ANN)
Parallel processing capability:
Artificial neural networks have a numerical value that can perform more than one task simultaneously.
Storing data on the entire network:
Data that is used in traditional programming is stored on the whole network, not on a database. The
disappearance of a couple of pieces of data in one place doesn't prevent the network from working.
Capability to work with incomplete knowledge:
After ANN training, the information may produce output even with inadequate data. The loss of
performance here relies upon the significance of missing data.
Having a memory distribution:
For ANN is to be able to adapt, it is important to determine the examples and to encourage the network
according to the desired output by demonstrating these examples to the network. The succession of the
network is directly proportional to the chosen instances, and if the event can't appear to the network in
all its aspects, it can produce false output.
Having fault tolerance:
Extortion of one or more cells of ANN does not prohibit it from generating output, and this feature
makes the network fault-tolerance.
BY
B SARITHA
4
