Artificial Neuron Network

What is ANN?

As we read in last article what is Perceptron is (an artificial Neuron) when all Neuron are connect together forming (group of 2 or more) are known as ANN. In ANN there are multiple layer of hiden layer ,and one output layer.

What are component of ANN?

In ANN :

The first layer is Input Layer where all the input are taken from the second layer to output layer are known as Hidden layer where all the Matrix calculation are done .Then there is Forward propogation where all the weights are updated and there is the main algorithm comes which known as BackPropogation in this algorithms all the weights are updated to make the the accuracy of model more and more.

There are diffrent type of ANN are:

1. Feedforward Neural Network(FNN)

   This is the most basic type of ANN where the information moves in on direction , from the input layer ,through the hidden layer , to the out put layer .there are no loops or cycle in this type.It is Mostly used in Regression and classification model.

2. Convolutional Neural Network(CNN)

   CNNs are specialy designed for image processing and computer vision task.They use Cconvolutional Layers which he that helps in automatically detecting features like features like edges ,shapes,and texture from images .CNNs are the blackbone of modern technologies like Face recognition and object detection.

3. Recurrent Neural Network(RNN)

   RNNs are use for sequential data like series, speech , and text. Unlike feedforward networks ,RNNs have loops which remember the previous outputs. This memory like structure iss helpful in language modelling and prediction tasks.

How does ANN learns?

The main goal of any ANN is to reduce the error between the actual output and predicted value.This learning process happens through:

• Forward Propogation:

  In this process the input is passed through the no. of layer of the network ,and prediction are made through based o current weights and biases.

• Loss Function:

  After Prediction , the error is calculated using loss function (like MSe,RMSE etc).This tells us how far our predicted values are from real values.

• Backpropogation:

  This is the main learning process of an ANN .The calculated error is propogated back through network and all the weights are upgraded through optimization techniques like Gradient descent .the aim of us is to minimize the error through this process .

Important Terms to Know

• Weights – These are the adjustable parameters in a neural network that influence the strength of connections between neurons.

• Bias – An extra parameter that helps the model shift the activation function.

• Activation Function – Functions like ReLU, Sigmoid, or Tanh which decide whether a neuron should activate or not.

• Epoch – One complete pass of the full training dataset through the network.

• Learning Rate – A small value that controls how much the weights should be updated in each step.

So as we know what is ANN is and its type in next article we are gonna learn how to create a model in next article .

Summary

An Artificial Neural Network (ANN) comprises interconnected neurons that form a model with input, hidden, and output layers. Key types include Feedforward Neural Networks (FNN) for regression and classification, Convolutional Neural Networks (CNN) for image processing, and Recurrent Neural Networks (RNN) for sequential data. ANN learns by minimizing error through forward propagation, a loss function, and backpropagation. Important concepts include weights, bias, activation functions, epoch, and learning rate.