In this section, we are going to see a practical implementation of a neural network. We will define the problem statement, then we will understand the dataset we are going to use to solve the problem, whereupon we will create the model in Keras to solve the problem. Once the model is created in Keras, we will convert it into a model that's compatible with Core ML. This Core ML model will be imported into an iOS application, and a program will be written to use this model and interpret the handwritten digits.
We are going to tackle the problem of recognizing handwritten digits through a machine learning model that we'll implement in an iOS mobile application. The first step is to have the database of handwritten digits that can be used for model training and testing.
The MNIST digits dataset (http://yann.lecun.com/exdb/mnist/) provides a database of handwritten digits, and has a training set of 60,000 examples and a test set of 10,000 examples...
