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  12M: Use case: Written character recognition → CNN Description of the MNIST handwritten digit recognition problem: The MNIST problem is a dataset for evaluating machine learning models on the handwritten digit classification problem Each image is a 28 x 28 pixel square  A standard split of the dataset is used to evaluate and compare the models, where 60000 images are used to train the model and a separate set of 10000 images are used to test it It is a digit recognition task. As such there are 10 digits or 10 classes to predict Excellent results achieve a prediction error of less than 1% Program: # Larger CNN for the MNIST Dataset from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.layers import Dropout from tensorflow.keras.layers import Flatten from tensorflow.keras.layers import Conv2D from tensorflow.keras.layers import MaxPooling2D from tensorflow.keras.utils import to...