# What is Backpropagation Neural Network : Types and Its Applications

As the name implies, backpropagation is an algorithm that back propagates the errors from output nodes to the input nodes. Therefore, it is simply referred to as “backward propagation of errors”. This approach was developed from the analysis of a human brain. Speech recognition, character recognition, signature verification, human-face recognition are some of the interesting applications of neural networks. The neural networks go through supervised learning, the input vector passing through the network produces output vector. This output vector is verified against the desired output. If the result doesn’t match with the output vector, an error report is generated. Based on the error report, weights are adjusted to get the desired output.

## What is an Artificial Neural Network?

An Artificial Neural Network employs supervised learning rule to become efficient and powerful. The information in neural networks flows in two different ways. Primarily, when the model is being trained or learning and when the model operates normally – either for testing or used to perform any task. Information in different forms is fed into the model through input neurons, triggering several layers of hidden neurons and reach the output neurons, which is known as a feedforward network.

As all the neurons do not trigger at the same time, the neurons that receive the inputs from the left are multiplied with the weights as they travel through hidden layers. Now, add up all the inputs from every neuron and when the sum exceeds a certain threshold level, the neurons that had remained silent will trigger and get connected.

The way the Artificial Neural Network learns is that it learns from what it had done wrong and does the right, and this is known as feedback. Artificial Neural Networks use feedback to learn what is right and wrong.

## What is Backpropagation?

**Definition:** Backpropagation is an essential mechanism by which neural networks get trained. It is a mechanism used to fine-tune the weights of a neural network (otherwise referred to as a model in this article) in regards to the error rate produced in the previous iteration. It is similar to a messenger telling the model if the net made a mistake or not as soon as it predicted.

Backpropagation in neural networks is about the transmission of information and relating this information to the error generated by the model when a guess was made. This method seeks to reduce the error, which is otherwise referred to as the loss function.

### How Backpropagation Works – Simple Algorithm

Backpropagation in deep learning is a standard approach for training artificial neural networks. The way it works is that – Initially when a neural network is designed, random values are assigned as weights. The user is not sure if the assigned weight values are correct or fit the model. As a result, the model outputs the value that is different from the actual or expected output, which is an error value.

To get the appropriate output with minimal error, the model should be trained on a pertinent dataset or parameters and monitor its progress each time it predicts. The neural network has a relationship with the error, thus, whenever the parameters change, the error also changes. The backpropagation uses a technique known as the delta rule or gradient descent to change the parameters in the model.

The above diagram shows the working of backpropagation and its working is given below.

- ‘X’ at the inputs reach from the preconnected path
- ‘W’, the real weights are used to model the input. The values of W are randomly allotted
- The output for every neuron is calculated through forwarding propagation – the input layer, hidden layer, and output layer.
- The error is calculated at the outputs using the equation Propagating backward again through output and hidden layers, weights are adjusted to reduce the error.

Again propagate forward to calculate the output and error. If the error is minimized, this process ends, or else propagates backward and adjusts the values of weight.

This process repeats until the error reduces to a minimum and desired output is obtained.

### Why We Need Backpropagation?

This is a mechanism used to train the neural network relating to the particular dataset. Some of the **advantages of Backpropagation** are

- It is simple, fast and easy to program
- Only numbers of the input are tuned and not any other parameter
- No need to have prior knowledge about the network
- It is flexible
- A standard approach and works efficiently
- It does not require the user to learn special functions

### Types of Backpropagation Network

There are two kinds of backpropagation networks. It is categorized as below:

#### Static Backpropagation

Static backpropagation is one type of network that aims in producing a mapping of a static input for static output. These kinds of networks are capable of solving static classification problems like optical character recognition (OCR).

#### Recurrent Backpropagation

The recurrent backpropagation is another type of network employed in fixed-point learning. The activations in recurrent backpropagation are fed forward till it attains a fixed value. Following this, an error is calculated and propagated backward. A software, NeuroSolutions has the ability to perform the recurrent backpropagation.

The key differences: The static backpropagation offers immediate mapping, while mapping recurrent backpropagation is not immediate.

### Disadvantages of Backpropagation

Disadvantages of backpropagation are:

- Backpropagation possibly be sensitive to noisy data and irregularity
- The performance of this is highly reliant on the input data
- Needs excessive time for training
- The need for a matrix-based method for backpropagation instead of mini-batch

### Applications of Backpropagation

The applications are

- The neural network is trained to enunciate each letter of a word and a sentence
- It is used in the field of speech recognition
- It is used in the field of character and face recognition

### FAQs

**1). Why do we need backpropagation in neural networks?**

This is a mechanism used to train the neural network relating to the particular dataset

**2). What is the objective of the backpropagation algorithm?**

The objective of this algorithm is to create a training mechanism for neural networks to ensure that the network is trained to map the inputs to their appropriate outputs.

**3). What is the learning rate in neural networks?**

The learning rate is defined in the context of optimization and minimizing the loss function of a neural network. It refers to the speed at which a neural network can learn new data by overriding the old data.

**4). Is the neural network an algorithm?**

Yes. Neural networks are a series of learning algorithms or rules designed to identify the patterns.

**5). What is the activation function in a neural network?**

The activation function of a neural network decides if the neuron should be activated/triggered or not based on the total sum.

In this article, the concept of Backpropagation of neural networks is explained using simple language for a reader to understand. In this method, neural networks are trained from errors generated to become self-sufficient and handle complex situations. Neural networks have the ability to learn accurately with an example.