What is Back Propagation and How does it work?

Before we get into the details of this blog, let me ask you a simple question - are you superhuman? From that look of confusion you have right there on your face, it is evident that you’re not - no one is. As a result, it isn't required that the weight values we choose while developing a Neural Network have to be right or that they are the best match for our model. 

 

But let’s say we have chosen some weight values at the beginning. However, our model output differs significantly from our real output, resulting in a large error value. In order to adjust the parameters (weights) in such a way that the error is minimized, our model has to be conditioned. 

 

Backpropagation is one such method of training our neural network model. To know how exactly backpropagation works in neural networks, keep reading the text below. So, let us dive in and try to understand what backpropagation really is.

 

 

Definition of Back Propagation

 

The core of neural network training is backpropagation. It's a technique for fine-tuning the weights of a neural network based on the previous epoch's error rate (i.e., iteration). By fine-tuning the weights, you may lower error rates and improve the model's generalisation, making it more dependable.

 

Backpropagation is a short form for "backward propagation of mistakes" in a neural network. It's a common way to train artificial neural networks. This method is useful for calculating the gradient of a loss function with respect to all of the network's weights.

 

A neural network is a collection of interconnected input/output (I/O) nodes. A loss function is used to express the level of precision that each node produces (error rate). 

 

The mathematical gradient of a loss function with respect to the other weights in the neural network is calculated via backpropagation. The calculations are then utilised to give low-error-rate artificial network nodes less weight than high-error-rate nodes.

 

To increase outputs, backpropagation employs a technique known as chain rule. Essentially, the technique conducts a backward pass through a network after each forward pass to modify the model's weights. (here).

 

Need for Backpropagation

 

Two passes are used to train a neural network: forward and backward. The network error is calculated at the end of the forward pass and should be as small as feasible.

 

The network did not learn adequately from the data if the current error is significant. What exactly does this imply? It means that the existing weights are insufficiently accurate to reduce network error and produce accurate predictions. As a result, network weights should be updated to reduce network error.

 

One of the deep learning algorithms responsible for changing network weights with the goal of lowering network error is the backpropagation algorithm. It's quite significant. 

 

 

Types of Backpropagation

 

There are two types of Backpropagation:

 

1. Static Backpropagation 

 

A static input is mapped to a static output in this network. Static classification problems like optical character recognition will be an appropriate domain for static backpropagation.

 

2. Recurrent Backpropagation

 

Another form of network used in fixed-point learning is recurrent backpropagation. In recurrent backpropagation, the activations are fed forward until they reach a fixed value. The error is then calculated and transmitted backwards. NeuroSolutions is a software that can do recurrent backpropagation. (Source)

 

The main distinction is that static backpropagation allows for immediate mapping, but recurrent backpropagation does not.

 

Also Read | Applications of neural network

 

 

How Does Backpropagation Work?

 

As we all know, training in artificial neural networks happens in stages. These are:

 

• Initialization

• Propagation in the opposite direction

• Error Detection Function

• Backpropagation

• Update on weight

• Iteration
   

The backpropagation method, which calculates the gradient of a loss function of the weights in the neural network to ensure the error function is minimal, is the fourth step of the procedure.

 

The backpropagation algorithm, on the other hand, does this through a series of Back Propagation Algorithm Steps, which include:

 

• Choosing Input and Output: The backpropagation algorithm's first step is to choose a process input and set the desired output.

 

• Setting Random Weights: After the input and output values have been determined, random weights are assigned in order to alter the input and output values. Following that, each neuron's output is estimated via forwarding propagation, which involves: 

 

• Input Layer 

• Hidden Layer

• Output Layer 

 

• Error Calculation: This is an important step that determines the total error by determining how far and suitable the actual output is from the required output. This is accomplished by calculating the output neuron's mistakes.

 

• Error Minimization: Based on the observations made in the previous step, the goal here is to reduce the error rate as much as possible so that accurate output can be supplied.

 

• Updating Weights and Other Parameters: If the error rate is large, the delta rule or gradient descent are used to adjust and update parameters (weights and biases) in order to lower the rate of error. 

 

By assuming a proper learning rate and propagating backwards from the output layer to the previous layer, this is done. This helps eliminate repetitive calculations of recurring errors, neurons, and layers by acting as an example of dynamic programming.

 

• Modelling Prediction Readiness: Finally, after the error has been optimised, the output is evaluated with appropriate testing inputs to ensure that the desired result is obtained.

 

To know more about how Backpropagation works, watch this:

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Backpropagation's Applications

 

Backpropagation and its derivatives, such as backpropagation across time, are widely used for training nearly all types of neural networks, and they have aided deep learning's recent rise in popularity. It is used in face recognition models and speech recognition models.

 

Sony Corporation of Japan produced an example implementation of a speech recognition system for English and Japanese that can run on embedded devices. The system is set up to only listen for a certain number of commands from the user. (Reference)

 

Also Read | Introduction to Graph Neural Network

 

 

Advantages of Backpropagation

 

Apart from using gradient descent to correct trajectories in the weight and bias space, another reason for the resurgence of backpropagation algorithms is the widespread use of deep neural networks for functions such as image recognition and speech recognition, in which this algorithm plays a key role.

 

But that isn't the case. This method has a number of other benefits, which are stated below:

 

• By deleting weighted linkages, it simplifies the network structure.

• Programming is quick and simple.

• It is not necessary to have any prior understanding of the networks.

• The characteristics of the function to be learned do not need to be specified.

• Allows for efficient gradient computation at each layer.

 

Conclusion

 

Backpropagation has become a vital aspect of neural networks, as it relies on this algorithm to become self-sufficient and capable of addressing complicated difficulties and issues, as seen in the preceding discussion. 

 

Furthermore, it allows neural networks to train accurately while remaining adaptable. The popularity of this method has grown to the point that it is used in the latest technologies such as natural language processing, audio recognition, image recognition, and more.

 

So, whether you're making a system that can correctly pronounce words and sentences or artificial neural networks, the Back Propagation Algorithm lies at the heart of both.

Also Read | Neural network programs/software