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mlclass-ex3
- 斯坦福机器学习公开课，第三次练习作业，向量化实现，logistic分类和神经网络-Stanford open class machine learning, the third practice operations, to quantify the realization, logistic and neural network classification
LMS
- 由美国斯坦福大学的Widrow和Hoff在研究自适应理论时提出的LMS算法，由于其容易实现而很快得到了广泛应用，成为自适应滤波的标准算法。-LMS algorithm has been widely used due to its easy to implement, become=ing the standard of the adaptive filtering algorithm.
machine-learning-ex5
- 斯坦福大学机器学习公开课CS229 课程作业5-Stanford University public courses CS229 machine learning coursework 5
machine-learning-ex1
- 斯坦福大学机器学习公开课CS229 课程作业4-Stanford University public courses CS229 machine learning coursework 4
machine-learning-ex2
- 斯坦福大学机器学习公开课CS229 课程作业2-Stanford University public courses CS229 machine learning coursework 2
Stanford-Machine-Learning
- 斯坦福大学机器学习课程个人学习笔记（下）斯坦福大学机器学习公开课笔记-Stanford machine learning courses personal study notes (below) machine learning open class at Stanford University notes
LMS
- 感知器和自适应线性元件在历史上几乎是同时提出的，并且两者在对权值的调整的算法非常相似。它们都是基于纠错学习规则的学习算法。感知器算法存在如下问题：不能推广到一般的前向网络中；函数不是线性可分时，得不出任何结果。而由美国斯坦福大学的Widrow和Hoff在研究自适应理论时提出的LMS算法，由于其容易实现而很快得到了广泛应用，成为自适应滤波的标准算法。-Least mean square error algorithm
mlclass-ex3-007
- BP神经网络框架，已完成的斯坦福公开课第四讲代码，运行正确，适合初学者实验用-BP NETWORK
machine-learning-ex4
- 斯坦福机器学习编程作业machine-learning-ex4，神经网络模型，Neural Networks Learning题目，满分，2015最新作业答案 matlab代码-Stanford machine learning Programming Exercise 4: machine-learning-ex4
machine-learning-ex5
- 斯坦福大学机器学习第六周，包括第六周的讲义以及作业代码ex5。-machine learning week6 ex5 - Regularized Linear Regression and Bias v.s.Variance
ex6
- 斯坦福机器学习公开课第七周SVM编程作业ex6-machine learning week7 assignment
ML
- 这是斯坦福大学公开课的笔记，机器学习的同学可以看一下，还是非常不错的。-This is the Stanford University open class notes, machine learning students can look at, or very good.
CHAI3D
- 这是一篇介绍CHAI3D的文档，供读者了解CHAI3D，具体的内容可以去斯坦福大学官网查看相关资料！-This is an introduction to the CHAI3D document, for the reader to understand the CHAI3D, the specific content can go to the official website of Stanford University to see the relevant information!
dragon.ply
- 斯坦福大学的点云数据，龙模型，对于学习点云来说是非常好的实验资料-The point cloud data of Stanford University, dragon model, for learning point cloud is very good experimental data
mlclass-ex2-jin
- 斯坦福大学机器学习源代码的一个例程，包括说明文档以及详细代码-A routine Stanford University machine learning source code, including documentation and code details
mlclass-ex3-jin
- 斯坦福大学机器学习源代码的一个例程，多组分类以及神经网络-Multi-class Classi cation and Neural Networks
mlclass-ex5-jin
- 正则线性回归和偏差(斯坦福大学机器学习例子，包括说明文档以及源代码)-Regularized Linear Regression and Bias v.s.Variance
image-dog
- 这是斯坦福大学深度学习中对于狗的图像处理的相关程序，想要了解更多，下面有网址！-Stanford Dogs Dataset - For more information about the dataset, please visit the dataset website: http://vision.stanford.edu/aditya86/ImageNetDogs/ If you use this dataset in a publica
ex1
- 斯坦福大学机器学习作业一完整代码，带注释-Machine Learning Stanford job a complete code with comments
machine-learning-ex1
- 斯坦福机器学习课程第一次练习代码,所有代码都在压缩文件里，仅供参考，经过提交成功了-code for the first exercise of Machine Learning course of Stanford University