官术网_书友最值得收藏!

Neural networks in R

We will build several neural networks in this section. First, we will use the neuralnet package to create a neural network model that we can visualize. We will also use the nnet and RSNNS (Bergmeir, C., and Benítez, J. M. (2012)) packages. These are standard R packages and can be installed by the install.packages command or from the packages pane in RStudio. Although it is possible to use the nnet package directly, we are going to use it through the caret package, which is short for Classification and Regression Training. The caret package provides a standardized interface to work with many machine learning (ML) models in R, and also has some useful features for validation and performance assessment that we will use in this chapter and the next.

For our first examples of building neural networks, we will use the MNIST dataset, which is a classic classification problem: recognizing handwritten digits based on pictures. The data can be downloaded from the Apache MXNet site (https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/data/mnist_csv.zip). It is in the CSV format, where each column of the dataset, or feature, represents a pixel from the image. Each image has 784 pixels (28 x 28) and the pixels are in grayscale and range from 0 to 255. The first column contains the digit label, and the rest are pixel values, to be used for classification.

主站蜘蛛池模板: 兴义市| 柳州市| 福建省| 昌都县| 宁强县| 千阳县| 二连浩特市| 桐乡市| 新营市| 洛浦县| 雅江县| 许昌市| 阳原县| 商城县| 环江| 湘阴县| 徐汇区| 木兰县| 南溪县| 安国市| 通辽市| 米林县| 盐源县| 双牌县| 台北县| 九江市| 汕尾市| 九寨沟县| 金阳县| 洪雅县| 鹤庆县| 崇阳县| 萨迦县| 霍州市| 大宁县| 樟树市| 桂东县| 固始县| 平阴县| 马公市| 桂林市|