Characters, Spirals and Unit Dynamics by training neural network model

Abstract

Deep learning is an integral part of machine learning by building a structure like the neural network in the human brain. Amounts of different neural network models have been emerged, improved, and transformed. This article will implement various neural network models for three different specific tasks (Japanese character recognition/double helix task/remote unit dynamics) and analyse the fine-tuning parameters process (different network layers, different types of network layers). The result is that generally, models with more layers would perform better. However, other parameter could have significant effects on the performance. For example, activation function, different numbers of hidden nodes, different initial weights, and different periods, etc.