Neural Network
If you are from an IT background, you might have faced some situations where you want your system to perform certain tasks in the same way as our mind do.
If a computer can perform such tasks with their own intelligence. How it sounds..?
In the World of AI ( Artificial Intelligence ) & ML (Machine Learning), we want our machines to think like human Brain.
As a Human Brain can learn things faster and can predict something on the basis of their past experiences, we want our machines to work in the same way.
How Human Brain work, what are the core unit of Brain (Neuron)..? Let’s find out ..=>
What is Neuron..
The basic working unit of the brain Neurons, also known as nerve cells, send and receive signals from your brain. While neurons have a lot in common with other types of cells, they’re structurally and functionally unique. Specialized projections called axons allow neurons to transmit electrical and chemical signals to other cells.
Human Nervous System
The nervous system is the part of an animal’s body that coordinates its behavior and transmits signals between different body areas. In vertebrates it consists of two main parts, called the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS contains the brain and spinal cord.
The nervous system helps all the parts of the body to communicate with each other. It also reacts to changes both outside and inside the body. The nervous system uses both electrical and chemical means to send and receive messages.
What is Neural Network..??
A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
Neural networks have a remarkable ability to retrieve meaningful data from imprecise data, that is used in detecting trends and extract patterns which are difficult to understand either by computer or humans. A trained NN can be made an “expert” in information that has been given to analyse and can be used for provide projections.
Advantages of Neural Networks
Some of the advantages of neural networks are listed below-
Self-Organisation: An ANN can generate its own representation of the information that it receives at the time of learning.
Real Time Operation: ANN calculations may be done simultaneously, and some special (hardware) devices are manufactured which take advantage of this capability.
Adaptive learning: Capability to learn how to solve tasks is based on the data given for training set.
Redundant Information Coding Through Fault Tolerance: Semi destruction of a network leads to degradation of corresponding performance. Moreover, some network will have the ability to retain data even when a major network damage occurs.
Working of NN
To understand neural networks, we need to break it down and understand the most basic unit of a Neural Network, i.e. a Perceptron.
What Is A Perceptron?
A Perceptron is a single layer neural network that is used to classify linear data. It has 4 important components:
Inputs
Weights and Bias
Summation Function
Activation or transformation Function
The basic logic behind a Perceptron is as follows:
The inputs (x) received from the input layer are multiplied with their assigned weights w. The multiplied values are then added to form the Weighted Sum. The weighted sum of the inputs and their respective weights are then applied to a relevant Activation Function. The activation function maps the input to the respective output.
Weights and Bias In Deep Learning
Why do we have to assign weights to each input?
Once an input variable is fed to the network, a randomly chosen value is assigned as the weight of that input. The weight of each input data point indicates how important that input is in predicting the outcome.
The bias parameter, on the other hand, allows you to adjust the activation function curve in such a way that a precise output is achieved.
Summation Function
Once the inputs are assigned some weight, the product of the respective input and weight is taken. Adding all these products gives us the Weighted Sum. This is done by the summation function.
Activation Function
The main aim of the activation functions is to map the weighted sum to the output. Activation functions such as tanh, ReLU, sigmoid and so on are examples of transformation functions.
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