资源列表
Softmax_exercise
- Softmax用于多分类问题，本例是将MNIST手写数字数据库中的数据0-9十个数字进行分类，其中训练样本有6万个，测试样本有1万个数字是0~9-Softmax for multi classification problems, the present case is the handwritten data MNIST digital 0-9, classification, training samples which have 60,000, there are 10,000 test
ExerciseSelf-Taught-Learning
- Soft-taught leaning是用的无监督学习来学习到特征提取的参数，然后用有监督学习来训练分类器.-Soft-taught leaning unsupervised learning is to learn the parameters of feature extraction, followed by supervised learning to train the classifier.
stacked-autoencoder
- 基于两层的层叠自编码的深度学习模型，前两层用于特征提取，再加一个Softmax分类器用于分类-Two stacked the depth of learning coding model based on the first two levels for feature extraction, coupled with a classifier for classifying Softmax
hrrp
- 简单的使用*.ffe文件返回值计算一维距离像的小程序-Simple to use* .ffe file returns the value calculated the one-dimensional image of the applet
5.c
- 一个整数，它加上100后是一个完全平方数，再加上168又是一个完全平方数，求该数-An integer, which is added after 100 is a perfect square, plus 168 is a perfect square, for the number of
trees
- 机器学习中的c4.5树的源代码，经测试，可以使用-The source code tree c4.5 in machine learning, through the test, can be used
main
- 有1、2、3、4个数字，能组成多少个互不相同且无重复数字的三位数？都是多少？-There are 1, 2, 3, 4 numbers, can be composed of a number of different and no duplication of the three digit number? How much is it?
email
- 机器学习算法的数据集，包含训练集和测试集。主要用于邮件分类-Machine learning algorithms of data sets, including training set and testing set.Mainly used for E-mail classification
logRegres---python
- 机器学习中的逻辑回归算法，经过测试，可以使用-Logistic regression algorithm of machine learning, through the test, you can use
horseColic
- 机器学习的训练样本和测试样本。用于机器学习算法。-Machine learning training samples and testing samples.Used for machine learning algorithms.
learning-data-mining-with-python
- 《python数据挖掘入门与实践》随书源代码，Chapter1-Chapter12.使用ipython notebook运行，包括社会媒体挖掘，作者归属，新闻语料分析，大数据处理等应用实例。-Python data mining entry and practice with the book source code, using Chapter1-Chapter12. IPython notebook operation, including social media mining, aut
System-Identification
- 系统辨识最小二乘法 梯度校正参数估计法 极大似然参数估计法 多变量系统参数估计-System Identification Least Square Method Gradient Correction Parameter Estimation Maximum Likelihood Parameter Estimation Multivariable System Parameter Estimation