I am a deep learning beginner recently reading this book "Deep learning with Python", the example explains the process of implementing a greyscale image classification using MNIST in keras, in the compilation step, it said,
Before training, we’ll preprocess the data by reshaping it into the shape the network expects and scaling it so that all values are in the [0, 1] interval. Previously, our training images, for instance, were stored in an array of shape (60000, 28, 28) of type uint8 with values in the [0, 255] interval. We transform it into a float32 array of shape (60000, 28 * 28) with values between 0 and 1.
Images stored in an array of shape (60000, 28, 28) of type uint8 with values in the [0, 255] interval. For my understanding, the values are between 0-255 of each px and storied as 3D matrix. Can someone explain why needs to "transform" it into the network expects by scaling it and make "all values are in the [0, 1]interval."?
Please also make suggestions if I didn't explain some parts correctly.