Artificial neural networks

Las artificial neural networks (ANN) o artificial neural networks (ANN), are systems of machine learning what, just like the human brain, are composed of artificial neural units interconnected.

ANNs are built from a set of input data, which are processed by a learning algorithm to produce an output model. This model can be applied to new input data to predict the desired output..

History of neural networks in computing

The history of neural networks in computing is relatively short., but the impact they have had is enormous. Although the concept of artificial neural networks dates back to the 1990s, 1940, it was in the years 1980 when they began to take shape. In 1986, artificial intelligence scientist Geoffrey Hinton and his colleagues published a paper that showed how artificial neural networks could be used to recognize patterns in data sets. This article became a turning point and the field of artificial intelligence got in motion.

Since then, Neural networks have undergone rapid development. Has been created new and better ways to learn, and have been discovered new applications for these powerful tools. Today, neural networks are being used in a wide variety of areas, from the natural language processing until the object recognition. And the impact is not limited to academia: neural networks are changing the world of technology, and the pace of this change it's just accelerating.

How Neural Networks Work

Artificial neural networks are based on a mathematical model of the way neurons work in the brain. Each neuron is connected to other neurons through links called synapses. The electrical activity travels along the axons of neurons transmitted to other neurons across synapses. Is electrical activity is what we call a nerve impulse.

The nerve impulses are the how the brain transmits information. Neurons receive these impulses y, in function of the amount of stimulation they receive, whether or not they send a nerve impulse to the neurons to which they are connected. In this way, neurons can communicate information with each other y, as a last resort, allow the brain to perform tasks As the thinking and learning.

Artificial neural networks

Artificial neural networks are based on the same principle that the functioning of neurons in the brain. Each artificial neuron it is connected to other artificial neurons via links called synapses. The electrical activity travel along the drivers of neurons and transmitted to other neurons across synapses. Is electrical activity is what we call a nerve impulse.

The nerve impulses are the how artificial neurons transmit information. Neurons receive these impulses y, in function of the amount of stimulation they receive, whether or not they send a nerve impulse to the neurons to which they are connected. In this way, artificial neurons can communicate information with each other y, as a last resort, allow the network to perform tasks As the thinking and learning.

machine learning

The machine learning is a field of artificial intelligence that focuses on how you can create systems that learn from data. The machine learning can be divided into two broad categories: the supervised learning and the unsupervised learning.

The supervised learning It is the type of learning in which tagged data provided. For example, if we want a neural network to learn to recognize images of cats, we would provide you with a set of images tagged as “cat” or “not cat”. The unsupervised learning It is the type of learning in which no tagged data provided. In this case, the neural network will have to teach itself to recognize patterns in data.

Neural networks today

Today, neural networks are being used in a wide variety of areas, from the natural language processing until the object recognition. And the impact is not limited to academia: neural networks are changing the world of technology, And the pace of this change is only accelerating.

In the field of natural language processing, neural networks are being used to develop chatbot systems increasingly sophisticated. These systems are being used more and more to provide assistance to customers of a company.

In the field of object recognition, neural networks are being used to develop image recognition systems more and more precise. These systems are being used more and more to recognize objects in images y perform tasks such as object tracking.

The future of neural networks

Although the field of neural networks has experienced rapid development in recent years, there is still much to discover. Are developing new ways of learning, and they are discovering new applications for these powerful tools. The future of neural networks is very promising y only limited by imagination.

How do neural networks work?

Artificial neural networks are made up of a large number of interconnected neurons. Each neuron is connected to other neurons through links that they transmit signs between them.

How do you learn a neural network??

The neural network learning takes place by adjusting link weights between the neurons. When a input pattern a la red, individual neurons activate or deactivate based on their weights.

How are neural networks used??

Artificial neural networks can be used to solve a wide variety of machine learning problems, as classification, regression, object detection and pattern recognition.

Applications of neural networks

Neural networks can be used to solve a wide variety of computer problems. Some of the most common applications are the following:

1. pattern recognition

Neural networks can be trained to recognize patterns in unstructured data, such as images or audio signals. This is very useful in applications such as speech recognition or object detection in images..

2. data classification

Neural networks can be trained to classify data into categories. For example, can be used to identify whether an image contains a cat or a dog. They can also be used to detect if a text message is spam or not.

3. robot control

Neural networks can be used to control robots. For example, robots have been trained to walk and balance. They are also being investigated so that they can control autonomous vehicles.

4. Time series prediction

Neural networks can be used to predict the behavior of a time series, like the price of a share in the stock market. This is useful for making investment decisions.

5. text generation

Neural networks can be used to generate text from a set of data. For example, they have been trained so that they can generate news from financial market data. They are also researching so that they can write poetry.

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