Neural networks: definition, pros and cons. Everything you need to know

Neural networks resemble the human brain and are used in many application fields. Find out all of their key features!

2022-07-14T10:30:06+00:00 Febraury 28th, 2023 | Author: Niccolò Cacciotti | Category:

Neural networks: definition, pros and cons. Everything you need to know

Neural networks resemble the human brain and are used in many application fields. Find out all of their key features!

2022-07-14T10:30:06+00:00 Febraury 28th, 2023 | Author: Niccolò Cacciotti | Category:

Home/Artificial Intelligence/Neural networks: definition, pros and cons. Everything you need to know

Artificial neural networks resemble the human brain and are used in many application fields. Find out all of their key features!

What a neural network is and isn’t


The world of Artificial Intelligence is as fascinating as it is complex. We recently explained what computer vision is and what its main tasks are. We have also finally defined the differences between Artificial Intelligence, machine learning and deep learning.  Now it is time to deepen another topic that is still difficult to understand.

We are talking about neural networks.

We almost always tend to confuse them with machine learning. So before we define what they are, let’s start by saying what they are not. Let’s make it clear right away.  

Neural networks are not synonymous with machine learning. Indeed, the latter is a subset of artificial intelligence that creates algorithms and uses large data sets to autonomously make models and approaches to solve problems

Thus, machine learning refers to programs that learn from data all by themself . 

Starting from this premise, we can introduce the concept of neural networks, also called artificial neural networks (ANN).

Neural networks are a specific type of machine learning model used to make decisions as the human brain does. They originated in the 1950s and were inspired precisely by the functioning of neurons in the human brain.

Therefore, how does a neural network work?

A neural network consists of a series of nodes that contain an input layer, one or more hidden layers and an output layer.  Each node connects to another and has a specific weight and threshold. If the output of a node is above the specified threshold value, that node is activated. As a result, it sends data to the next layer in the network and so on. Otherwise, no data is passed to the next layer in the network.

What the main types of neural networks are


Having explained what neural networks are, we can go on to learn what the main types of neural networks are. 

First of all, we can state that there are different types of neural networks. They are distinguished by type and structure

For the sake of simplicity they are divided into two macro categories depending on whether the neurons in the various layers of the network can be connected to each other. They are: feedforward and feedback, or also recurrent. Let us find out their differences.

Feedforward neural networks are only able to create neural connections in a specific direction, from input to output. That’s where the name “feedforward” comes from. For example, Single-Layer Perceptron, Multi-Layer Perceptron and Convolutional Neural Network belong to this category.

Feedback neural networks, on the other hand, also allow connections between neurons in the same layer. One example is Long Short Term Memory (LSTM). 

Let’s review the main features and applications of the previously named neural network architectures. 

Single-Layer Perceptron

It is the simplest type of neural network and also the simplest of the feedforward networks. It consists of two layers of neurons and accepts only binary input and output.  This network can solve basic logic operations and is used for pattern classification.

Multi-Layer Perceptron

Multi-Layer Perceptron is another type of feedforward network. It is an extended perceptron with one or more layers hidden between the input and output layers. Due to its structure, this network is capable of solving any logical operation.

Convolutional Neural Network

These networks are a subset of the multilayer networks. They consist of at least five layers. Pattern recognition is performed on each layer, where the result of one layer is transferred to the next layer. These networks have several applications in image and video recognition.

Long short term memory

It is a special type of recurrent neural network. LSTM is capable of learning long-term dependencies, especially in sequence prediction problems. This network has feedback connections and can process the entire sequence of data and individual data points. LSTM neural networks are able to project time series to the future based on what happened in the past. Its main applications are speech, handwriting recognition and modeling predictions.

It is possible to see that among the various networks there is always improvement and advancement at the level of architecture and operation. This is also effectively demonstrated by the various application fields becoming more and more articulated.

What the pros and cons of neural networks are


As we have just seen from the various areas of use of neural networks. it becomes apparent how widely they are deployed.

This stems, of course, from the great benefits that neural networks possess. What are they?

We present them below: 

  • ability to work in parallel: so they can handle and process large amounts of data quickly;
  • noise tolerance: they are able to operate correctly despite inaccurate or otherwise incomplete inputs;
  • solution to specific and complex problems: predictive, simulation and classification modeling are the main fields of use for neural networks;
  • ease of updating: a well-implemented neural network is able to update itself when there is new data.

As in all cases, artificial neural networks also have limitations that do not allow its use in various cases.

For example, we can find among its disadvantages the following ones: 

  • computation that cannot be fully analyzed: they can provide correct outputs, but they do not allow the individual processing steps that determine them to be examined;
  • no a priori certainty: it is not possible to know from the outset whether a problem will be solved;
  • output and solution: the outputs provided by the inputs are not always the solution to the problem; 
  • long learning period: iterations take a very long time because aspects, such as number and complexity of input, algorithm used, must be considered.

Surely, thanks to new studies in this field, we will see the development of new neural networks that will be able to overcome these limitations that now exist.

They will be able to perform increasingly complex human tasks. Will they also be able to perform them better than we can?  


© Copyright 2012 – 2023 | All Rights Reserved


Author: Niccolò Cacciotti, Head of AI Department

Home/Artificial Intelligence/Neural networks: definition, pros and cons. Everything you need to know

Artificial neural networks resemble the human brain and are used in many application fields. Find out all of their key features!

What a neural network is and isn’t


The world of Artificial Intelligence is as fascinating as it is complex. We recently explained what computer vision is and what its main tasks are. We have also finally defined the differences between Artificial Intelligence, machine learning and deep learning.  Now it is time to deepen another topic that is still difficult to understand.

We are talking about neural networks.

We almost always tend to confuse them with machine learning. So before we define what they are, let’s start by saying what they are not. Let’s make it clear right away.  

Neural networks are not synonymous with machine learning. Indeed, the latter is a subset of artificial intelligence that creates algorithms and uses large data sets to autonomously make models and approaches to solve problems

Thus, machine learning refers to programs that learn from data all by themself . 

Starting from this premise, we can introduce the concept of neural networks, also called artificial neural networks (ANN).

Neural networks are a specific type of machine learning model used to make decisions as the human brain does. They originated in the 1950s and were inspired precisely by the functioning of neurons in the human brain.

Therefore, how does a neural network work?

A neural network consists of a series of nodes that contain an input layer, one or more hidden layers and an output layer.  Each node connects to another and has a specific weight and threshold. If the output of a node is above the specified threshold value, that node is activated. As a result, it sends data to the next layer in the network and so on. Otherwise, no data is passed to the next layer in the network.

What the main types of neural networks are


Having explained what neural networks are, we can go on to learn what the main types of neural networks are. 

First of all, we can state that there are different types of neural networks. They are distinguished by type and structure

For the sake of simplicity they are divided into two macro categories depending on whether the neurons in the various layers of the network can be connected to each other. They are: feedforward and feedback, or also recurrent. Let us find out their differences.

Feedforward neural networks are only able to create neural connections in a specific direction, from input to output. That’s where the name “feedforward” comes from. For example, Single-Layer Perceptron, Multi-Layer Perceptron and Convolutional Neural Network belong to this category.

Feedback neural networks, on the other hand, also allow connections between neurons in the same layer. One example is Long Short Term Memory (LSTM). 

Let’s review the main features and applications of the previously named neural network architectures. 

Single-Layer Perceptron

It is the simplest type of neural network and also the simplest of the feedforward networks. It consists of two layers of neurons and accepts only binary input and output.  This network can solve basic logic operations and is used for pattern classification.

Multi-Layer Perceptron

Multi-Layer Perceptron is another type of feedforward network. It is an extended perceptron with one or more layers hidden between the input and output layers. Due to its structure, this network is capable of solving any logical operation.

Convolutional Neural Network

These networks are a subset of the multilayer networks. They consist of at least five layers. Pattern recognition is performed on each layer, where the result of one layer is transferred to the next layer. These networks have several applications in image and video recognition.

Long short term memory

It is a special type of recurrent neural network. LSTM is capable of learning long-term dependencies, especially in sequence prediction problems. This network has feedback connections and can process the entire sequence of data and individual data points. LSTM neural networks are able to project time series to the future based on what happened in the past. Its main applications are speech, handwriting recognition and modeling predictions.

It is possible to see that among the various networks there is always improvement and advancement at the level of architecture and operation. This is also effectively demonstrated by the various application fields becoming more and more articulated.

What the pros and cons of neural networks are


As we have just seen from the various areas of use of neural networks. it becomes apparent how widely they are deployed.

This stems, of course, from the great benefits that neural networks possess. What are they?

We present them below: 

  • ability to work in parallel: so they can handle and process large amounts of data quickly;
  • noise tolerance: they are able to operate correctly despite inaccurate or otherwise incomplete inputs;
  • solution to specific and complex problems: predictive, simulation and classification modeling are the main fields of use for neural networks;
  • ease of updating: a well-implemented neural network is able to update itself when there is new data.

As in all cases, artificial neural networks also have limitations that do not allow its use in various cases.

For example, we can find among its disadvantages the following ones: 

  • computation that cannot be fully analyzed: they can provide correct outputs, but they do not allow the individual processing steps that determine them to be examined;
  • no a priori certainty: it is not possible to know from the outset whether a problem will be solved;
  • output and solution: the outputs provided by the inputs are not always the solution to the problem; 
  • long learning period: iterations take a very long time because aspects, such as number and complexity of input, algorithm used, must be considered.

Surely, thanks to new studies in this field, we will see the development of new neural networks that will be able to overcome these limitations that now exist.

They will be able to perform increasingly complex human tasks. Will they also be able to perform them better than we can?  


© Copyright 2012 – 2023 | All Rights Reserved


Author: Niccolò Cacciotti, Head of AI Department