Backpropagation in neural network: how does it work?

Neural networks are a type of machine learning that is increasingly being developed and applied. Within this system, backpropagation represents a method for calculation that is used in algorithms designed for training artificial neural networks.

In this article, we are going to go through the details of everything related to creating and training artificial neural networks, a type of machine learning that requires Python programming for its development. This represents an important process within supervised learning that is essential for you to know, especially if you are interested in becoming a professional in this field.

If you want this, you will need training in programming. Because of this, this article will also address the preparation you need to be able to work as a machine learning algorithm programmer, which you can achieve through a Python Programmer course with a specialization in Machine Learning. Want to know more? Keep reading and discover everything you need to know!

 

What is backpropagation in neural network?

Artificial neural networks are a type of machine learning technique that aims to mimic how the human brain works. In order to do this, nodes (neurons) are connected to each other in different layers and are designed to try to imitate the learning model of the human brain. It is a complex process within the field of AI that is increasingly being developed, the generation of antagonistic networks being a good example of this.

In this context, a key element for programming certain algorithms for neural networks is the backpropagation algorithm. This calculation method is used, as we’ve mentioned above, to train systems for this type of automatic learning. A two-phase method based on an adaptation-propagation pattern is used for this.

There are many ways in which artificial neural networks can be trained and they all start with programming training algorithms. Python is the most used language to achieve this.

This is a particularly important process since, as neural networks are trained, nodes in the intermediate layers learn to organize themselves. This way, each of these nodes is capable of learning to recognize different characteristics of the input data.

Thanks to the backpropagation method, neural networks are able to identify incomplete or arbitrary data patterns and find the most appropriate solution for the problem that has been presented to them, since they will be able to find a pattern that is similar to the characteristics that they have learned to recognize during training. In other words, this algorithm can be used to detect errors in processes that involve the use of neural networks.

 

How does the backpropagation method work in neural networks?

The training of neural networks is a complex process that involves different stages. The backpropagation method is the fourth stage of the process which, at the same time, consists of different phases:

• Choice of input and output: this represents the first step in the operation of the algorithm, as it’s the moment where an input is determined for the entire backpropagation process until the desired output is achieved.
• Configuration: once the input and output values have been settled, the algorithm proceeds to assign a series of secondary values that allow it to modify parameters within each layer and node that make up the neural network.
• Error calculation: at this point, the total error is determined after performing an analysis of the nodes and layers of the neural network.
• Error minimization: once the errors are detected, the algorithm proceeds to minimize their effect on the entire neural network.
• Parameter update: if error rates are excessively high, the backpropagation method tries reducing them by adjusting and updating the parameters.
• Modeling for prediction: after optimizing for errors, the backpropagation calculation method evaluates the appropriate test inputs to ensure that the desired result is achieved.

 

What is this calculation method used for in the context of machine learning?

One of the main goals of developing neural networks is to adjust the weight of each of the nodes in order to minimize potential errors in the learning process. The backpropagation algorithm is used to determine how much influence each of the network nodes has in these errors.

As we have seen above, the calculation method used in this type of algorithm is what makes it make sense. In fact, what the error analysis allows is to determine the first node and the first error. From this point onwards, the algorithm makes a reverse tour to detect other sensitive points that might also be involved in making mistakes. This allows you to identify the problems and apply the pertinent solutions.

The concatenation of errors in neural networks arises as a consequence of the original first error, which then overlaps and affects the ensuing layers.

The input parameters of the nodes that make up each of the layers of the neural networks can influence the outputs towards the following layers and the nodes that comprise them. On the other hand, by applying the backpropagation algorithm, all errors can be traced. Thanks to this, it is possible to correct the configuration of the parameters for each of the nodes that influence subsequent layers and nodes.

 

Learn Python programming for neural networks!

Now you know what a backpropagation neural network is, how it works and what its purpose is. It’s time for you to learn to program and train algorithms for machine learning.

With Tokyo School’s Python Programmer course with a specialization in Machine Learning, you can achieve this. Our preparation will help you acquire all the necessary knowledge to be able to work as a programmer for machine learning.

Fill out the form below to get more information. Become a Tokyer! Dare to take the next step, get the right training and join an industry that is forecasted to grow spectacularly in the coming years. We can’t wait to meet you!