In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.

本文目的是分析神经网络的隐藏层数对网络性能的影响，为了展示这种影响，我们在MNIST数据集上应用了不同层数的神经网络，并观察不同层数和不同迭代次数下ANN精度的变化以进行比较和对比。

Study and Observation of the Variations of Accuracies for Handwritten
  Digits Recognition with Various Hidden Layers and Epochs using Neural Network
  Algorithm

使用神经网络算法研究和观察手写数字识别在不同隐藏层数和纪元下的准确性变化