Aryan Raj, a student with roll number 18700120047 studying in CSE 1 semester 5, has chosen the topic of explaining Neural Networks in detail for his AI subject. Neural Networks are computing systems inspired by biological neural networks that can perform tasks using large amounts of data, and are called artificial neural networks. The network is trained to produce desired outputs and use different models to predict future results from data. Nodes in neural networks are interconnected similar to a human brain. There are three main types of learning in neural networks: supervised, reinforcement, and unsupervised learning. The architecture of neural networks can be single layer feedforward, multi-layer feedforward, or recurrent. Neural networks have advantages like fault tolerance, parallel processing,

1. NAME – ARYAN RAJ
ROLL – 18700120047
SEC – CSE 1
SEMESTER – 5
SUBJECT – A.I
TOPIC - Explain in detail the Neural Network
Approach.

2. What is Neural
Networks?
• The computing systems inspired by biological
neural networks to perform different tasks with a
huge amount of data involved is called artificial
neural networks or ANN.
• The network is trained to produce the desired
outputs, and different models are used to predict
the future results with the data.
• The nodes are interconnected so that it works like
a human brain

3. Understanding Neural
Network
• Neural networks are trained and taught just like a
child’s developing brain is trained. They cannot be
programmed directly for a particular task. Instead,
they are trained in such a manner so that they can
adapt according to the changing input.
• There are three methods or learning paradigms to
teach a neural network.
1. Supervised Learning
2. Reinforcement Learning
3. Unsupervised Learning

4. 1. Supervised Learning
As the name suggests, supervised learning means in the
presence of a supervisor or a teacher.
It means a set of a labeled data sets is already present
with the desired output, i.e. the optimum action to be
performed by the neural network, which is already
present for some data sets.
The machine is then given new data sets to analyze the
training data sets and to produce the correct output.

5. 2. Reinforcement Learning
In this, learning of input-output
mapping is done by continuous
interaction with the environment
to minimize the scalarindex of
performance.
In this, insteadof a teacher, a
critic converts the primary
reinforcement signal, i.e. the
scalarinput received from the
environment, into a heuristic
reinforcement signal (higher
quality reinforcement signal)
scalarinput.
This learning aims to minimize
the cost to go function, i.e. the
expectedcumulative cost of
actions taken over a sequence
of steps.

6. 3. Unsupervised
Learning
• As the name suggests, there is no teacher
or supervisor available.
• In this, the data is neither labeled nor
classified, and no prior guidance is
available to the neural network.
• In this, the machine has to group the
provided data sets according to the
similarities, differences, and patterns
without any training provided beforehand.

7. Architecture of Neural Network
• There are basically three types of architecture of
the neural network.
1. Single Layer feedforward network.
2. Multi-Layer feedforward network.
3. Recurrent network.

8. 1. Single- Layer Feedforward
Network
• In this, we have an input layer of source nodes
projected on an output layer of neurons. This
network is a feedforward or acyclic network.
2. Multi-Layer Feedforward
Network
In this, there are one or more hidden layers except for the input
and output layers. The nodes of this layer are called hidden
neurons or hidden units. The role of the hidden layer is to
intervene between the output and the external input.

9. 3. Recurrent Networks
• A recurrent is almost similar to a
feedforward network. The major
difference is that it at least has one
feedback loop. There might be zero or
more hidden layers, but at least one
feedback loop will be there.

10. Advantages of
Neural Network
• Can work with incomplete information once trained.
• Have the ability of fault tolerance.
• Have a distributed memory
• Can make machine learning.
• Parallel processing.
• Stores information on an entire network.
• Can learn non-linear and complex relationships.
• Ability to generalize, i.e. can infer unseen
relationships after learning from some prior
relationships.