A "Hello World" Into Image Recognition with MNIST

To begin, we'll load the library Keras and other necessary inputs: Next, we'll load the MNIST dataset and split it into X train, X test, Y train, and Y test data: Next, we can outline some important variables for image loading and training. The data needs to be converted to a 32-bit float and standardized. Now that the data is ready, we can define the model architecture. After the model architecture has been defined, it must be compiled. Compiling a model outlines the loss function, optimizer, and metrics.