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Artificial intelligence has become a common tool in today’s business world, and it’s no wonder. AI has been programmed to solve problems that have baffled human beings since the dawn of time. Think of all the things that can be automated — from fetching the best price for a car to predicting what’s in a package with the use of AI.

Unfortunately, however, AI is not perfect. Deep learning is a specific type of AI that is excellent at identifying patterns, but it suffers from a problem called “overfitting,” which can cause AI to become too dependent on a specific set of data and lose its ability to generalize.

Data augmentation is one of the most effective ways to combat overfitting. In this article, we’ll explore how data augmentation works and how it can be used in machine learning to improve the performance of a model.

What is data augmentation?

Data augmentation is a creative process that involves manipulating data. It involves creating artificial data that is similar, but not identical, to the data you are working with.

In the context of machine learning, data augmentation is a process that makes a dataset more complex by adding random noise to it or by changing the values of the data. This allows the model to learn to recognize patterns in the augmented dataset instead of simply memorizing the original dataset.

Data augmentation creates more data, so you can train your model with a larger dataset and improve the performance of the model. For example, if you have a dataset of 10,000 pictures and you add random noise to each of them, you will end up with a dataset of 20,000 pictures. This process is called “data generation.”

In the context of machine learning, data augmentation is a process that makes a dataset more complex by adding random noise to it or by changing the values of the data.

Data augmentation can also be used to prevent overfitting. If you have a model with 20 parameters, but you create a dataset with 100,000 pictures, the model will be able to learn to recognize patterns in the augmented dataset instead of simply memorizing the original dataset.

How do you use data augmentation in machine learning?

Data augmentation is not a new concept in the world of machine learning. It was first introduced in 1974 by Frank Rosenblatt, a pioneer in AI, as a way to create artificial data to increase the size of a dataset in order to train a model.

Data augmentation usually involves creating artificial data by adding random noise to the original data. For example, you can take a dataset of pictures of a cow and add noise to the images to create a dataset of pictures of a cow and a bunch of random objects.

Another approach that can be used to create artificial data is to change the values of the data. For example, you can apply a random rotation to a dataset of images and then add noise to the rotated images.

How does data augmentation work?

Data augmentation can be used in two ways. It can be done by the model or by a third party, depending on the type of data that you are working with.

In the context of a model, you can add noise to the original data and then do the same thing to the data generated from the original data. In essence, you create artificial data that is similar to the original data and feed it to the model.

To do this, you need to generate a new set of data that is similar to the original data. To generate the new data, you need to randomly select values for the new data.