What is a Neural Network?
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Imagine that you want to learn how to translate text from English into Spanish. You learn languages by memorizing words and phrases, their meanings, and the context in which they are used. Based on this experience, you will be able to translate new texts that you have never seen before.
Another case is the classification of cats and dogs. Just as a person learns to distinguish them from examples seen in life, so a neural network can learn to distinguish them from such examples.
The neural network does something similar. It learns from examples - it can be texts, images, sounds, any data that we want it to process. A neural network, just like a person learns a language, tries to identify patterns in this data.
It then uses these patterns to perform tasks such as classification (determining which category an object belongs to), regression (predicting a numerical value such as the price of a house), or generation (creating new content based on the learned patterns). This process of training a neural network using examples is called supervised learning and this is the most common way to train it.
Note
When a neural network is trained, labeled examples are fed as input. When we want to get a prediction from it, inputs are not labeled.
Here's a demonstration of a Neural Network designed to identify drawings of cats and dogs. It addresses a classification problem by taking an input of an unknown class and producing an output with the identified class. Experience it firsthand to understand its functionality.
LMB (Left Mouse Button) - to draw.
Shift + LMB - to erase.
Join us in this learning journey, and we will guide you through the process of creating similar neural networks, step by step.
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Introduction to Neural Networks
Introduction to Neural Networks
What is a Neural Network?
Let's Start
Imagine that you want to learn how to translate text from English into Spanish. You learn languages by memorizing words and phrases, their meanings, and the context in which they are used. Based on this experience, you will be able to translate new texts that you have never seen before.
Another case is the classification of cats and dogs. Just as a person learns to distinguish them from examples seen in life, so a neural network can learn to distinguish them from such examples.
The neural network does something similar. It learns from examples - it can be texts, images, sounds, any data that we want it to process. A neural network, just like a person learns a language, tries to identify patterns in this data.
It then uses these patterns to perform tasks such as classification (determining which category an object belongs to), regression (predicting a numerical value such as the price of a house), or generation (creating new content based on the learned patterns). This process of training a neural network using examples is called supervised learning and this is the most common way to train it.
Note
When a neural network is trained, labeled examples are fed as input. When we want to get a prediction from it, inputs are not labeled.
Here's a demonstration of a Neural Network designed to identify drawings of cats and dogs. It addresses a classification problem by taking an input of an unknown class and producing an output with the identified class. Experience it firsthand to understand its functionality.
LMB (Left Mouse Button) - to draw.
Shift + LMB - to erase.
Join us in this learning journey, and we will guide you through the process of creating similar neural networks, step by step.
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