Human beings are intelligent because We have the ability to learn. We learn from our experiences and, As we have more, We are improving our skills. In the same way, the artificial neural networks (ANN, for its acronym in English) can be trained to Mimicking human learning. They learn from the data provided to them and, as they receive more information, are improving their results.
For Train an artificial neural network, you are provided with a series of data and you are asks you to perform a specific task. As the neural network works on the task, Adjusts its internal parameters to try to produce the right results. This process is called machine learning o Machine Learning. The goal of machine learning is Make the neural network optimally adapt to the data to produce the right results.
There's Two main approaches to training an artificial neural network: the supervised learning and the unsupervised learning. In the supervised learning, The neural network is provided with data that have already been previously tagged. Namely, The neural network is told what result to expect for each piece of data. In the unsupervised learning, No labels are provided to the data. Instead, The neural network is asked to Look for patterns in your data and learn from them.
The supervised learning is the most commonly used for training an artificial neural network. This approach is believed to be more effective because Provides the neural network with guidance on what results to expect. In this way, The neural network can adjust its internal parameters more efficiently to produce the right results. The unsupervised learning is more difficult to use because no labels are provided to the data. Nevertheless, Some researchers believe this approach is more effective because Allows the neural network to learn more naturally, as humans do.
Usually, the supervised learning It is the most effective approach to training an artificial neural network. Nevertheless, the unsupervised learning May be helpful in some cases. For example, if you have a large set of data that is not labeled, Unsupervised learning can be a good way to extract useful information from them.
What kind of problems can be solved with AI?
Artificial intelligence (AI) is a field of computer science that focuses on the development of intelligent agents capable of performing complex tasks.. These intelligent agents are built from algorithms and machine learning techniques., that allow them to learn and improve their skills as they gain more experience.
Machine Learning Algorithms
Machine learning algorithms are an important part of artificial intelligence. Used to build models from data, enabling intelligent agents to make data-driven decisions. There are different types of machine learning algorithms, that can be used to solve different types of problems.
Problems that can be solved with AI
Artificial intelligence can be used to solve a wide variety of problems. Some examples of problems that can be solved with AI are:
- Optimization issues: AI can be used to find the optimal solution to a problem, whether a math or other problem.
- Assignment issues: AI can be used to assign tasks to different agents optimally.
- Prediction problems: AI can be used to predict the behavior of a system, for instance, The behaviour of financial markets.
- Diagnostic problems: AI can be used to diagnose diseases, for instance, using medical imaging.
- Control issues: AI can be used to control complex systems, for instance, Robot Systems.
Machine learning techniques
Machine learning techniques are used to build models from data. These models can be used to make decisions or perform actions.. There are different machine learning techniques, that can be used to solve different types of problems.
Problems that can be solved with machine learning techniques
Machine learning techniques can be used to solve a wide variety of problems. Some examples of problems that can be solved with machine learning techniques are:
- Optimization issues: Machine learning techniques can be used to find the optimal solution to a problem, whether a math or other problem.
- Assignment issues: Machine learning techniques can be used to assign tasks to different agents optimally.
- Prediction problems: Machine learning techniques can be used to predict the behavior of a system, for instance, The behaviour of financial markets.
- Diagnostic problems: Machine learning techniques can be used to diagnose diseases, for instance, using medical imaging.
- Control issues: Machine learning techniques can be used to control complex systems, for instance, Robot Systems.