Neural networks are programs that mimic the human brain by learning from large amounts of data. They use simulated neurons that are connected together to form networks, similar to the human nervous system. Neural networks learn by adjusting the strengths of connections between neurons, and can be used to perform tasks like pattern recognition or prediction. Common neural network training algorithms include gradient descent and backpropagation, which help minimize errors by adjusting connection weights.

1. What is a Neural Network?
• The term ‘Neural’ is derived from the human nervous system’s basic
functional unit ‘neuron’ or nerve cells that are present in the brain and
other parts of the human body.
• A neural network is a group of algorithms that certify the underlying
relationship in a set of data similar to the human brain.
• The neural network helps to change the input so that the network
gives the best result without redesigning the output procedure.

2. Parts of Neuron and their Functions

3. Artificial Neural Network
• Artificial Neural Network (ANNs) are programs designed to solve
any problem by trying to mimic the structure and the function of our
nervous system.
• Neural networks are based on simulated neurons, Which are
joined together in a variety of ways to form networks.
• Neural network resembles the human brain in the following two
ways: -
• A neural network acquires knowledge through learning.
• A neural network’s knowledge is stored within the interconnection
strengths known as synaptic weight.

5. Types of Neural Networks

7. Artificial Neuron Model

8. Terminology

9. Artificial Neural Network Model

10. Training Algorithms For Artificial Neural Networks
• Gradient Descent Algorithm
• This is the simplest training algorithm used in case of supervised
training model. In case, the actual output is different from target
output, the difference or error is find out. The gradient descent
algorithm changes the weights of the network in such a manner to
minimize this mistake.

11. Back Propagation Algorithm
• It is an extension of the gradient-based delta learning rule. Here, after
finding an error (the difference between desired and target), the error
is propagated backward from the output layer to the input layer via
the hidden layer. It is used in case of Multilayer Neural Network.

12. Artificial Neural Network Architecture
• A typical Neural Network contains a large number of artificial
neurons called units arranged in a series of layers.

14. • Input layer – It contains those units (Artificial Neurons) which
receive input from the outside world on which network will learn,
recognize about or otherwise process.
• Output layer – It 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 the hidden layer is to transform the input into something
that output unit can use in some way.
• Most Neural Networks are fully connected that means to say each
hidden neuron is fully linked to every neuron in its previous
layer(input) and to the next layer (output) layer.

15. Neural Network Applications