What are Neural Networks (NN)?

Estefania Teixeira

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Technology

Updated:

8/24/26

Posted:

1/17/24

By emulating the neuronal structure of the human brain, Neural Networks became key within Machine Learning subfields. This Deep Learning edge's training relies on data to understand and comprehend human patterns and thought processes. We know Neural Networks can sometimes be hard to understand. In this article, we'll dive into the types, history, features, pros and training of this sub-field of ML.

What are Neural Networks?

A Neural Network comprises a series of algorithms focused on recognizing the underlying relationships in a data set. The goal here is to imitate the cognitive process of the human brain. In sum, a Neural Network learns from data in the same way humans learn from experience. These neuron systems are capable of modifying inputs to achieve the goal of generating the best possible outcome without redesigning the output criteria. Within NNs, a neuron is a math function that processes an input to deliver an outcome. With this ground, it allows users to classify information according to priorly selected architectures. This work similar to statistical methods such as curve fitting and regression analysis. NNs contain layers of interconnected nodes, and each node is known as a perceptron, similar to multiple linear regression. Perceptrons can introduce a network that performs numerous linear transformations, as in linear regression within an activation function that introduces nonlinearities to the output.

The History of Neural Networks

Neural Networks have a rich and interesting history that dates back to the 1940s. First of all, the creation of this subset of ML was inspired by the human brain's structure and functionality. However, NN remained a theoretical model with limited practical applications during the first stage. During the 80s and 90s, the experts moved the development of algorithms to a new, more efficient phase. In 1943, Walter Pitts and Warren McCulloch published "A Logical Calculation of the Ideas Immanent in Nervous Activity." The investigation analyzed how the human brain could produce complex patterns. What's more, its goal was to reach the point of being simplified to a binary logical structure with only true/false connections.

After this theory, the lead role in the development fell to Frank Rosemblatt of the Cornell Aeronautical Laboratory in 1958. He introduced weights to McCulloch's and Pitt's work and leveraged it to demonstrate how a computer could use NNs to make inferences about detected images. This process allows the application of Neural Networks in pattern recognition, image processing and Natural Language Processing. They're currently used for tasks such as voice recognition, computer vision and predictive modeling. Nonetheless, other uses include discovering new medications, identifying trends within the financial market and performing massive scientific calculations.

How Does Neural Networks Work?

Since the human brain is the principal inspiration for Neural Networks, experts translated its architecture into an AI model. NNs contemplate artificial neurons, known as nodes, and Artificial Neural Network that use software systems to solve math problems. A Neural Network can recognize each aspect of the available data set and how each relate to each other. In the process of doing so, NNs can also find complex patterns in large volumes of data. To be more specific, the basic architecture of a Neural Network has interconnected artificial neurons in three layers:

1. Input Layer: Information from the outside world enters the artificial neural network from the input layer. The input nodes must process the data, classify it and then pass it to the next layer.
2. Hidden Layer: These layers take input using whether other input or hidden layers. Each hidden layer analyzes the output of each before layer. They're processed with greater emphasis and then sent to the next layer.
3. Output Layer: The end layer is the one capable of providing the final results that involve the entire data processing. If you have a binary classification problem (yes/no), the output layer will have an output node whose result will be 1 or 0. In contrast, if you have a multiclass classification problem, the output layer can include more than one output layer. Each node integrates a connection with other nodes that answer to another layer. Likewise, each bond generated has a specific weight. This means that each neuron has a particular connection weight according to importance compared with other inputs. When all the values of the input layer nodes are multiplied (along with their weight) and summed up, a new value corresponding to the hidden layer is generated. This process is known as a linear transform. Hidden layers have a "activation" function capable of determining whether a node will become "active" and how "active" it might be. This process allows the network to learn nonlinearities from data.

Imagine you are going to make coffee. The neurons, in this case, will be the water. Coffee, milk, and other ingredients, like caramel, are part of the starting point. Following the indications, the amount of each element represents the weight. You can add the sugar and caramel once you put the coffee in the coffee maker with the water. All the ingredients will mix and take another shape. This process of transformation represents the neurons with its "activation functions." The components emerge the hidden layer, and the heating means the activation process that produces the result: coffee.

Types of Neural Networks

There are various Neural Network types according to how data flows from the input node to the output node. In the next section, you can observe the main types of this sub-field of Machine Learning.

• Fully Connected Neural Networks: A fully connected neural network comprises several fully combined layers connecting every neuron in a single layer to every neuron in the next. The main advantage of fully connected networks is that they are "structure agnostic," meaning no specific input assumptions are required. While this makes fully connected networks very broadly applicable, they perform worse than special-purpose networks customized to a specific problem.
• Recurrent Neural Networks: These NNs are capable of saving the output from the processing nodes and feeding the results into the model. Likewise, in this way, Neural Network models learn to predict the layer's outcome. Each node corresponds to Recurrent Neural Networks that work as memory cells, calculating and operating implementation. These models, at the same time, correlate to Deep Learning algorithms, often containing various hidden layers. Recurrent Neural Networks remember each part of the processed information to reuse it later. If the network makes incorrect predictions, the system learns and follows work to achieve the right prediction during the backpropagation phase. This type can be useful in circumstances such as text-to-speech software.
• Convolutional Neural Networks: A Convolutional NN model uses a variation of multilayer perceptrons, specifically a simple model corresponding to a biological neuron in an Artificial Neural Network. Its layers can create feature maps and register a region of a much larger image normally divided into rectangles. In addition, their networks lie in image recognition in most advanced Artificial Intelligence applications. Their common uses include facial recognition, signal processing and image classification.
• Deconvolutional Neural Networks: Deconvolutional Neural Networks simply operate in the opposite direction of Convolutional Neural Networks. The network's application is to detect items that might have been identified as important by a Convolutional Neural Network. Yet, these elements probably went through a discarding process during the execution of the Convolutional Neural Network. This Neural Network approach is present in image analysis and object detection.
• Transformer Neural Networks: Transformers are a type of neural network architecture that is becoming increasingly popular. In fact, OpenAI used transformers in their language models and DeepMind in AlphaStar, a program that defeated a professional Starcraft player. Transformers are essential for tackling the issue of sequence transduction, also known as neural machine translation. It includes any task that converts an input sequence to an output sequence and contains elements such as speech recognition, text-to-speech conversion, etc.

How To Train Neural Networks?

Training an artificial Neural Network implies large amounts of data. From the simplest perspective, this training uses data to provide information and tell the network the desired outcome. Suppose you want to build a network capable of identifying bird species. The initial training must be rooted in images of birds and animals that are not birds, including the integration of airplanes and any flying object. Each input would have a companion, specifically a matching identification, such as the name of a specific bird, or additional data, such as "not bird" or "not animal."

Responses should allow you to achieve an adjustable model according to its internal weights to learn how to guess the right bird species with the greatest accuracy. Imagine that the node layers Alpha, Beta, and Gamma tell the note Delta that the current image input corresponds to a Hawk, but node Epsilon says it's a Condor. You'd say that the training program confirms that it is, in fact, a Hawk. The Delta node will reduce the relevance it gives to the Epsilon input while increasing the importance you place on Alpha, Beta, and Gamma data.

Where to Use Neural Networks?

NNs contemplates various uses, with applications in financial operations, business planning, trading, business analysis, product maintenance, etc. They have also got a generalized adoption in business applications such as market forecasting and research solutions, fraud detection and risk assessment. NNs can, for instance, evaluate data-based business opportunities. In finance, NNs can process large amounts of transaction data, leveraging ML in security-driven manners. This feature is key to understanding of trading volume, trading range and asset volatility expectations for specific investments. Lastly, the healthcare field leverages NNs in medical diagnosis via image classification, process and control quality and identification of chemical compounds, etc. We can also include artificial vision, voice recognition, Natural Language Processing and Generation, sentiment analysis in NNs applications!

Conclusion

Neural Networks are integrated and complex systems that substantially advance Artificial Intelligence (AI). They have the potential to transform industries ranging from healthcare to banking by producing more accurate, faster, and deeper forecasts to improve the decision-making process and automate jobs. However, they are not without problems, such as the need for large amounts of data and computing power and the difficulty in understanding their complicated operation. Despite these obstacles, their advantages make them an intriguing field of study and application, considering the types of NN and the basic elements that make them up. As technology advances, we should expect to see even more creative use cases with an integration level more relevant to our daily lives.