Neural networks have been widely used in several areas, such as speech recognition, image classification, time series prediction, among others. This is because they are able to learn complex patterns from raw data, becoming more accurate as they are trained with more examples.
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Imagine you have an application that recognizes images of animals and you want to teach the application to recognize a dog. You show the app several images of dogs and say, “That’s a dog.” The app uses the neural network to analyze the characteristics of dog images and, over time, learns to identify a dog in other images.
As you show more and more examples of dogs, the app will get better and better at recognizing dogs.
Neural networks have become increasingly popular in recent years, thanks to advances in computational power and the availability of training data. If you want to know more about the subject, you can check out the book “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville, considered a reference in the field, or the article “A Few Useful Things to Know About Machine Learning” by Pedro Domingos , which provides an accessible introduction to the topic.
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