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Machine-Learning
- 斯坦福大学的Andrew Ng讲述的机器学习课程讲义-Stanford Andrew Ng tells the machine learning course notes
SAE
- 深度学习中稀疏编码的C语言程序,是根据斯坦福深度学习的教程MATLAB的代码改写的-Depth learning sparse coding in C language program is based on the Stanford deep learning tutorial MATLAB code rewrite
Exercise1-Sparse-Autoencoder
- 网址：http://deeplearning.stanford.edu/wiki/index.php/Exercise:Sparse_Autoencoder斯坦福深度学习的教程，这个是稀疏编码的的练习，可以直接运行-URL: http://deeplearning.stanford.edu/wiki/index.php/Exercise:Sparse_Autoencoder Stanford deep learning tutorial, this is a sparse coding exer
Exercise2-Vectorization
- http://deeplearning.stanford.edu/wiki/index.php/Exercise:Vectorization。斯坦福深度学习的教程，练习2的代码-http://deeplearning.stanford.edu/wiki/index.php/Exercise:Vectorization. Stanford deep learning tutorials, exercises 2 code
Exercise3-PCA-in-2D
- http://deeplearning.stanford.edu/wiki/index.php/Exercise:PCA_in_2D，斯坦福深度学习教程的代码-http://deeplearning.stanford.edu/wiki/index.php/Exercise:PCA_in_2D, Stanford depth tutorial code
Exercise4-PCA-and-Whitening
- http://deeplearning.stanford.edu/wiki/index.php/Exercise:PCA_and_Whitening斯坦福的深度学习的教程的练习，是关于数据预处理的-http://deeplearning.stanford.edu/wiki/index.php/Exercise:PCA_and_Whitening Stanford deep learning tutorial exercises about data preprocessing
Exercise5-Softmax-Regression
- 斯坦福深度学习教程中关于softmax regression的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行-Stanford deep learning tutorial exercises on softmax regression code, source code need to fill all places, all the full complement of the code, the handwriting recognitio
Exercise6-Self-Taught-Learning
- 斯坦福深度学习教程中关于Self-Taught的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行-Stanford deep learning tutorial exercises on Self-Taught code, source code need to fill all places, all the full complement of the code, the handwriting recognition into the pat
Exercise7-stacked-autoencoder
- 斯坦福深度学习教程中关于stacked autoencoder的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行-Stanford deep learning tutorial exercises on stacked autoencoder code, source code need to fill all places, all the full complement of the code, the handwriting recognit
Exercise8-linear-decoder
- 斯坦福深度学习教程中关于linear decoder 的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行-Stanford deep learning tutorial exercises on linear decoder code, source code need to fill all places, all the full complement of the code, the handwriting recognition into
2sum_hash
- 斯坦福机器学习公开课作业2sum的作业答案，python实现，运行结果已经经过验证-Stanford machine learning open class jobs 2sum job answer, python implementation, the results have been proven
algo0004
- 斯坦福机器学习公开课作业求逆序数的作业答案，python实现，运行结果已经经过验证-Stanford Machine Learning Homework publicly inverse number of job seeking answers, python implementation, the results have been proven
algo004
- 斯坦福机器学习公开课作业求最小割的作业答案，python实现，运行结果已经经过验证-Stanford Machine Learning Homework for the minimum cut open job answer, python implementation, the results have been proven
algo004
- 斯坦福机器学习公开课作业快速排序的作业答案，python实现，运行结果已经经过验证-Stanford machine learning open class jobs quicksort job answer, python implementation, the results have been proven
algo004
- 斯坦福机器学习公开课作业图的最小强联通的作业答案，python实现，运行结果已经经过验证-Stanford machine learning open class job the minimal intensity Unicom jobs answer, python implementation, the results have been proven
zlsqr
- 求解大型系数矩阵方程的最小二乘解，包括实数方程组和复数方程组。来自斯坦福Systems Optimization Laboratory- Implementation of a conjugate-gradient type method for solving sparse linear equations and sparse least-squares problems
Locally_weighted
- 斯坦福大学公开课第三课中涉及的局部加权线性回归，用matlab实现了在不同带宽下的对比-Stanford open class third lesson involved locally weighted linear regression, using matlab to achieve a comparison of different bandwidths
ex1_003
- matlab实现的推荐系统，融合机器学习的方法，出自斯坦福大学吴恩达-matlab implementation of recommendation system, the integration of machine learning methods, from Stanford University, Wu Enda
machine-learning-lecture-notes
- andrew ng教授的机器学习讲义，全英文，详细的介绍了斯坦福大学机器学习课程的内容-andrew ng Professor machine learning lectures, in English, a detailed descr iption of the Stanford machine learning course content
starter
- 斯坦福大学 cs294a 人工智能课程 练习一代码答案-stanford university cs 294a artificial intelligence course exercise 1 code