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Digit recognition

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Neural networks can establish connections between input and output through autonomous learning.

The challenge consists in skillfully preparing the input (feature engineering) and in setting up adequate network structures: architecture of the neural layers, ConvNet filters, activation functions.

A classic application is the recognition of handwritten digits:

Examples of use cases in industrial practice are the classification of objects on images, the detection of problematic financial transactions or the automatic topic assignment in large text corpora.

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