In this post, we'll learn how to preprocess data for use with TensorFlow.js and Node.js.
Data preprocessing is a critical step in machine learning. It's often said that data preprocessing is 80% of the work in machine learning. The reason for this is that the quality of the data has a huge impact on the performance of the machine learning algorithm.
There are many different ways to preprocess data. The methods you use will depend on the type of data you have and the machine learning algorithm you're using.
In this post, we'll focus on two methods of data preprocessing:
Normalization is a technique that is used to rescale data so that it is within a specific range. There are many different ways to normalize data, but the most common method is to rescale the data to the range [0, 1].
This can be done using the min-max method, which rescales the data so that the minimum value is 0 and the maximum value is 1.
To do this, we first need to find the minimum and maximum values of the data. We can then use the following formula to rescale the data:
x' = (x - min) / (max - min)
where x is the original data, x' is the rescaled data, min is the minimum value, and max is the maximum value.
We can use the min-max method to rescale the data in our training set. We can then use the rescaled data to train our machine learning algorithm.
Data augmentation is a technique that is used to artificially increase the size of the training data. This is done by creating new data points from the existing data.
There are many different ways to do data augmentation. The most common method is to use random transformations to create new data points.
For example, we could randomly rotate an image by a small amount to create a new data point. We could also randomly crop an image to create a new data point.
Data augmentation is a powerful technique that can be used to improve the performance of machine learning algorithms.
In this post, we learned about two methods of data preprocessing: normalization and data augmentation. We also learned how to implement these methods in TensorFlow.js and Node.js.