- Neural networks are a type of machine learning algorithm that is inspired by the human brain. Although they may appear complex, they are made up of basic structures and components that work together to process information and produce an output.
- However, let’s take a closer look at what they are, how they work and the thousands of applications they have today.
In the field of artificial intelligence, a concept known by very few stands out, but it is really the basis on which great tools that you know like ChatGPT, among many others, are based: neural networks.
The essence of a neural network lies in its ability to learn. Just as humans gain knowledge through experience, neural networks learn from data.
This learning process involves the adaptation of the internal parameters of the neural network based on the information presented to them. By exposing your network to a rich data set, you can develop the ability to recognize patterns, make predictions, and improve your capabilities over time.
However, what really is a neural network? How do they work and what role do they play today?
What is a neural network?
A neural network is a series of algorithms or instructions that, at their core, mimic the basic design of the human brain. Made up of interconnected neurons, these fundamental units receive information, process it and generate a response.
They are organized in different layers : an input layer that receives the data, several hidden layers that process this data and an output layer that gives the final decision or response, which is the one you end up seeing and it is so incredible to you because it is just What you were looking for.
For example, you upload a photo with an image of a cat to a tool like ChatGPT, and ask what animal it is. This will process it and give you a response stating that, indeed, it is a cat.
The most important thing about them is their ability to learn autonomously, without the need for a programmer to continually adjust the nuts of the machine. Once you manage to train it well and make it perfect, even if you add new data in the future, it is the key to its success.
This is how they work: the cat image example
However, for this tool to really work as we want and the output layer is spot on, it is necessary to gradually adjust each neuron with weights and biases in a range from 0 to 1. Weights decide what information is most important, and biases help refine those decisions. As the network learns, these are adjusted to improve it.
Let’s go back to the cat image example and imagine that you are teaching a neural network to differentiate between images and tell whether what it sees is a cat or not. Values closer to 1 indicate greater importance, while values closer to 0 indicate less importance.
Here weight can represent the importance of pointed ears. If the weight is close to 1, the network will consider pointed ears as a key trait to identify a cat. As for the bias, if it is higher, the network might be more inclined to identify cats in general, regardless of pointy ears.
After adjusting this and seeing the result, you evaluate the accuracy of your result and whether you were really right or not. In this case, the loss function will measure the difference between the neural network’s prediction and the actual response, between 0 and infinity. The smaller this difference, the smaller the loss, and vice versa. For example, 10 or even 0 will be better than 50.
In this case, what is sought is to keep this loss as low as possible during training, precisely adjusting its weights and biases to make accurate predictions.
Why are neural networks important?
Neural networks are important for several reasons, primarily due to their ability to solve complicated pattern recognition and processing problems.
- Complex information processing: Neural networks can process large amounts of information and learn complex patterns in data sets. This makes them effective in tasks such as image recognition and natural language processing, among others.
- Adaptability and continuous learning: Unlike traditional algorithms, neural networks can continuously learn and adapt as they are presented with new information.
- Pattern recognition: They are perfect at recognizing patterns in unstructured data. This is key in applications such as computer vision, where objects in images must be identified, or in natural language processing to understand meaning in texts.
- Task automation: For example, in autonomous driving, neural networks can learn to recognize traffic signs and obstacles without continuous programming.
- Decision improvement: In applications such as real-time decision making, neural networks can quickly analyze data and make predictions based on previously learned patterns, improving the speed and accuracy of decision making.
- Advances in medical research: In fields such as medicine, neural networks are used to analyze large sets of medical data, aiding in more accurate diagnoses and identifying patterns that might not be evident to doctors.
- Resource Optimization: They can optimize resource usage by learning efficiency patterns in processes, which can have a significant impact on operational efficiency in various industries.
Several types of Neural Networks
Different neural networks have different applications, depending on the use that is necessary to give them for data processing. The most important are the so-called feedforward, recurrent and convolutional.
‘Feedforward’ Neural Networks (FNN)
- Simple explanation: Imagine a straight line where information moves in only one direction, like a train going from one station to another without turning back.
- Common use: They are like the “bread and butter” and are used in tasks such as image recognition and classification.
Convolutional Neural Networks (CNN)
- Simple explanation: think of a flashlight that illuminates small areas of an image at a time. CNN focuses on specific parts of an image, capturing important details.
- Common use: perfect for object recognition in images, such as in security camera applications.
Recurrent Neural Networks (RNN)
- Simple explanation: like a chain of text messages where each message depends on the previous one. RNNs remember previous information, useful for data streams.
- Common use: Great for tasks with sequential data, such as predicting the next element in a sequence, such as words in a paragraph.
As you can see, neural networks are very powerful tools that have proven effective in all types of sectors, from the technology you use every day to those small scientific and medical advances that seek to improve everyone’s lives. They are still an area of active research and development, so there is still a lot of ground to cover and improve.
