Neural Networks: What They Are, Types and Applications in 2025
Franco Brutti
Who would have thought a few years ago that today we would be trying to mimic human neural networks with our own technologies?
These advances have sparked a revolution in a wide variety of fields, from computer vision to machine translation to business decision making.
As we move into the 21st century, it’s essential to understand how these data processing structures work and how they transform our world in different work-related areas, from detecting objects in images, to analyzing sentiment in human language, to playing games and much more.
What are neural networks?
Neural networks are a type of machine learning model inspired by the structure and functioning of the human brain that has become popular in recent years, especially with the advent of Artificial Intelligence.
These networks are designed to perform specific information processing tasks, such as pattern recognition, classification, regression and content generation, among other activities.
The fundamental idea behind neural networks is to simulate the way neurons in the human brain process and transmit information, so they are composed of basic units called artificial neurons or nodes, which are organized in interconnected layers.
The strength of neural networks lies in their ability to learn from the data they receive during the training process, where the network automatically adjusts the weights and connections between neurons to minimize the difference between its predictions and the desired responses.
Types of neural networks
There are several types of neural networks, each designed for specific tasks and adapted to different types of data and, to tell the truth, there is a great variety of them, about 10 or 12 depending on who you ask, but we will tell you about the most popular ones:
Feedforward Neural Networks (FNN)
Feedforward neural networks, also known as direct feedforward networks, are the most basic type of neural network because in them information flows in only one direction, from the input layer through one or more hidden layers to the output layer.
Each neuron in one layer is connected to all neurons in the next layer, allowing feature transformation and pattern extraction. These networks are commonly used in classification and regression tasks, where the network structure can be adapted according to the complexity of the task.
Convolutional Neural Networks (CNN)
Convolutional neural networks are designed to process grid-like data, such as images and videos, so they use convolutional layers that apply filters to detect local patterns in the input data, such as edges and textures.
This makes them effective in computer vision tasks, such as object recognition and image segmentation. Consequently, CNNs have revolutionized the ability of machines to understand and analyze visual content.
Recurrent Neural Networks (RNN)
Recurrent neural networks are designed to work with sequences of data, where order and time dependence are important. Unlike FNNs, RNNs have feedforward connections that allow them to maintain short-term memory of previous inputs.
This makes them suitable for natural language processing tasks, such as machine translation and text generation, as well as for time-series predictions, such as weather forecasting.
Long Short-Term Memory Neural Networks (LSTM)
Long Short-Term Memory (LSTM) networks are a variant of RNNs, but designed to address the problem of gradient fading, which is common in traditional RNNs.
LSTMs have a more complex architecture that allows them to learn long-term dependencies in data streams. This makes them useful in applications where long-term dependency is essential, such as machine translation and coherent text generation.
Neural Networks With Long-Term Memory (GRU)
GRU (Gated Recurrent Unit) neural networks are another variant of RNNs that simplify the LSTM architecture, often resulting in faster training. Despite their simplicity, GRUs have demonstrated excellent performance in natural language processing tasks such as text generation and sentiment analysis. They are also a popular choice when short- and long-term memory is required in data streams.
3 layers of neural networks
Like a human brain, neuronal networks have different sections and each of them has a function, characteristic and activity. While there are many other layers, the 3 fundamental ones are:
Input layer
The input layer is the first stage of the neural network and its main function is to receive the input data which can be features, numerical values, pixels of an image or any other type of information relevant to the task the network is designed to perform.
Each input feature is associated with a neuron in this layer. For example, in a neural network for handwritten digit recognition, each neuron in the input layer might represent a pixel in an image of a digit.
Sometimes, it’s necessary to perform preprocessing of the input data before feeding it to the network. This could include normalizing the values, extracting features, or any other step necessary to improve the performance of the network.
Hidden layers
The hidden layers are the core of the neural network, where most of the processing and computation occurs, because they are responsible for transforming and combining the input features to learn complex patterns and meaningful representations of the data.
In addition, the number of hidden layers and the number of neurons in each layer are hyper parameters that can vary according to the network architecture. Deep neural networks have multiple hidden layers and have been shown to be effective in solving complex tasks.
Each neuron in a hidden layer applies an activation function to its weighted input. This introduces a nonlinearity into the network, allowing it to learn nonlinear relationships in the data.
Output layer
The output layer is the last stage of the neural network and produces the final results of the network, which depend on the specific task being addressed. For example, in an image classification network, the output layer may produce the probability of belonging to different classes.
The number of neurons in the output layer is usually related to the number of output classes or dimensions in the task. For example, in a binary classification task, there may be one neuron in the output layer that represents the probability of belonging to one of two classes.
The activation function in the output layer depends on the task, since for example, in binary classification problems, the sigmoid function is used in most cases, while in multiclass classification problems, the softmax activation function is common.
Where are neural networks used?
This technology has gained popularity in a short time, so it’s used in a wide variety of applications in diverse fields due to its ability to learn and model patterns from data. Some of the areas and applications in which they are used are:
Computer Vision: in computer vision applications neural networks are used to recognize objects in images and videos, as well as to perform semantic segmentation tasks.
Natural Language Processing (NLP): here neural networks are fundamental for automatic translations, sentiment analysis and text generation. They are also the basis for virtual assistants such as Siri and Alexa.
Speech recognition: neural networks are applied in the interpretation of voice commands for virtual assistants and voice recognition systems in various applications.
Games and entertainment: in the field of games and entertainment, neural networks are used in reinforcement learning algorithms and in the automatic generation of artistic and musical content.
Health and medicine: in medical diagnostics neural networks contribute through medical imaging and in the search for promising chemical compounds in drug development.
Finance: in the financial field, neural networks are used in the analysis of financial data and the prediction of trends in stock markets and other assets.
Industry and manufacturing: neural networks are used for process control and predictive maintenance of equipment and machinery.
Robotics: neural networks enable robots to see and understand their environment, which is crucial for navigation and interaction in changing environments.
Automation: neural networks help automate repetitive tasks and processes in companies and services, improving efficiency.
Science and research: neural networks are used in simulations and modeling of natural phenomena, helping to advance scientific knowledge.
Which companies are already using neural networks?
When a technology becomes established, it’s difficult for its use not to spread among large companies, but you may not have heard until now who is using them, how or why, so here is a brief list.
Adobe: uses neural networks in its graphic design and image editing products, such as Photoshop, to improve image quality and provide advanced image processing functions.
Twitter: uses neural networks for detecting inappropriate content, classifying relevant tweets and recommending content in users' timelines.
General Electric: uses neural networks in predictive maintenance applications to monitor and prevent failures in industrial equipment and motors.
Siemens: applies neural networks in industrial automation and manufacturing process optimization, helping to increase efficiency and reduce costs.
NVIDIA: known for its high-performance GPUs, develops hardware and software to train and run neural networks, driving artificial intelligence applications in a wide range of industries.
Ford: Ford Motor Company employs neural networks in the research and development of autonomous vehicles, contributing to real-time decision making for autonomous driving.
Alibaba: uses neural networks in e-commerce applications, such as product classification and user experience optimization on its online shopping platform.
Sony: applies neural networks in products such as digital cameras for face detection and autofocus, as well as in improving video and audio quality in its multimedia products.
Pinterest: uses neural networks in pin recommendation and visual search, which helps users find relevant content on its platform.
Can you use neural networks to work?
Neural networks and artificial intelligence are increasingly being used in the work environment to improve efficiency and automate some tasks, although in most cases human personnel are still used to oversee the efficiency of these technologies. Because, yes, they facilitate tasks such as:
Email processing.
Document organization.
Image Recognition.
Natural Language Processing (NLP).
Voice recognition
Autonomous driving
Fraud detection
Content recommendation
Supply chain optimization
Health and diagnostics
Simulations and modeling
Content generation
Task automation
And maybe in this short list we have fallen short. And yes, they do facilitate tasks, but they still need the human eye to supervise in some cases certain tasks and this is where you come in.
This brings us to the following question: If neural networks arrive to make your job easier: would you learn how to use them to be more efficient?
Faqs neural networks
What are neural networks?
They are machine learning models inspired by the human brain, designed to process data, recognize patterns, and generate content.
What are the main types of neural networks?
The most popular are Feedforward, CNN, RNN, LSTM, and GRU, each adapted to different data types and tasks.
In which fields are neural networks used?
They are applied in healthcare, finance, robotics, computer vision, natural language processing, entertainment, and industrial automation.
Which companies already use neural networks?
Companies like Adobe, Twitter, Siemens, NVIDIA, Ford, Alibaba, and Pinterest apply them in products, processes, and services.
Can neural networks replace human jobs?
Not completely. They automate tasks and improve efficiency but still require human supervision for critical processes.
How can I learn to work with neural networks?
You can take online master’s programs, practical courses, or certifications in AI to learn how to apply these technologies in your field.
