Neural networks are a key concept in artificial intelligence that mimics the functioning of the human brain. They are used in many applications, such as image recognition, natural language processing, and even video games.
This article, written by the Yiaho team, aims to explain simply what a neural network is in the field of AI, how it works, and where it is used.
What is a neural network in artificial intelligence?
A neural network is a set of artificial neurons that work together to solve complex problems. It is widely used in the field of machine learning.
In a neural network, we can talk about several layers. A “layer” is a group of neurons that process data in a specific way. Each layer has a different role in the learning and information processing process.
Here are the main types of layers:
- Input layer: This is the first layer where data enters the network. For example, to recognize handwritten digits, each pixel of the digit image is an input. This layer transforms raw data into a format that the network can use.
- Hidden layers: Between the input layer and the output layer, there are one or more hidden layers. These layers perform calculations to analyze and modify the data. Each neuron in these layers makes decisions based on the information it receives and uses activation functions to allow the network to learn more complex relationships.
- Output layer: This is the final layer that produces the end result. For example, if the network needs to identify a digit, this layer will say whether it’s a “3”, a “5”, etc. The network is trained with labeled data to improve the accuracy of its predictions by adjusting its parameters.
Neural networks are essential in modern machine learning models. They are used in many applications, such as image recognition and language processing, similar to what ChatGPT does, for example.
In summary, each layer of a neural network has an essential role in transforming input data into final results, thus allowing the network to learn and make predictions.
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How do neural networks work?
Imagine you’re trying to learn to recognize objects in images. Here’s how a neural network would proceed:
- Input data: You show the network thousands of images of objects, each correctly labeled.
- Learning process: The network analyzes each image. The neurons in the hidden layers examine details, such as shape, color, or textures, and try to understand what distinguishes one type of object from another.
- Weight adjustment: Each neuron assigns weights to the features it identifies. If a neuron finds that a certain color is important for recognizing an object, it will increase the weight for that feature. If another feature is less useful, it will decrease its weight.
- Prediction and correction: Once the network is trained, it can receive new images. It will then give its prediction about the visible object. If the network is wrong, it will adjust its weights to improve in future attempts.
Examples of neural network applications
Neural networks are used in many fields:
- Image recognition: Applications like Google Photos use neural networks to identify objects or faces in images.
- Autonomous vehicles: Driverless vehicles use neural networks to analyze images of their environment and make driving decisions.
- Automatic translation: Services like Google Translate use neural networks to translate sentences from one language to another more accurately and fluently.
Read also: OpenAI o1: Introduction to this high-performance AI language model
A fascinating and essential technology for AI
Neural networks are a fascinating technology that mimics the functioning of the human brain to learn and solve problems. Thanks to their ability to analyze complex data, they play an essential role in many of today’s technological innovations.
By understanding how these networks work, we can better appreciate the advances in artificial intelligence and their applications in our daily lives. If you want to learn more about this exciting topic, feel free to explore further by browsing our AI glossary!
