Course Code: rneuralnet
Duration: 14 hours
Prerequisites:

Programming 在任何推荐的编程语言中。

Overview:

本课程介绍如何使用 R-project 软件将神经网络应用于现实世界中的问题。

Course Outline:

Neural Networks 简介

  1. Neural Networks 是什么
  2. 目前神经网络的应用现状
  3. Neural Networks 与回归模型的比较
  4. 监督式与非监督式学习

可用套件概述

  1. nnet、neuralnet 及其他
  2. 套件之间的差异及其限制
  3. 神经网络的可视化

应用 Neural Networks

  • 神经元与神经网络的概念
  • 大脑的简化模型
  • 神经元的机会
  • XOR 问题与值的分布特性
  • Sigmoidal 函数的多态性
  • 其他激活函数
  • 神经网络的构建
  • 神经元连接的概念
  • 神经网络作为节点
  • 建立网络
  • 神经元
  • 层
  • 比例
  • 输入与输出数据
  • 范围 0 到 1
  • 标准化
  • 学习 Neural Networks
  • 反向传播
  • 传播步骤
  • 网络训练算法
  • 应用范围
  • 估算
  • 近似可能性问题
  • 范例
  • OCR 与图像模式识别
  • 其他应用
  • 实施神经网络模型预测上市股票价格
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