搜索资源列表
machine-learning-courses_stanford
- 斯坦福大学Andrew Ng讲授的机器学习课程的全程讲义及录音对应的文字资料,对许多常用机器学习算法有着深入系统的讲解。-The notes of Machine learning courses in Stanford University。
rwrelease.tar
- Andrew N.G. 教授于2011年机器学习国际会议(ICML)发表了文章Code for On Random Weights and Unsupervised Feature Learning， 文件是paper的实现代码，非常具有参考价值-Professor Andrew NG 2011 machine learning Article Code for On the Random Weights and Unsupervised Feature Learning Internation
release.tar
- 斯坦福大学计算机系Andrew N.G教授于2011年CVPR发表文章Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis，文件是实现的code，很有用哦-Andrew NG, Professor of the Department of Computer Science at Stanford University published in
notes-of-AndrewNg
- 这是斯坦福大学著名教授Andrew Ng 的机器学习备课笔记。-The notes of Professor Andrew Ng in Stanford University about mechine learning
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
cs229-notes12
- 这是 Andrew Ng 机器学习讲义《Reinforcement Learning and Control》,包含了离散和连续MDP的内容，比官网的课件全。（官网的缺乏连续MDP部分）-This is Andrew Ng Machine Learning Materials " Reinforcement Learning and Control" , contains a discrete and continuous MDP content than the officia
Machine-Learning-exercises_finished
- andrew NG上的课程machine learning 的习题解答，这些习题对于机器学习入门还是非常有帮助的，值得学习。-This my answer to the programming exercises of Machine Learning by Andrew NG,this files are very useful to the people who want to study with Machine Learning.
machine-learning
- 斯坦福大学有关于机器学习的英文讲义，主讲人为Andrew Ng。-Stanford University about machine learning English handouts, speaker artificially Andrew Ng.
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
machine learning 的线性回归算法
- andrew ng 教授在machine learning 上的线性回归算法matlab代码，对于学习机器学习的新手是一个很好的入门课程
matlab
- 聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有
UFLDL-1
- Andrew Ng老师的关于unsupervised feature learning and deep learning的入门电子书-An introduction to unsupervised feature learning and deep learning from Andrew Ng
machine-learning_key_andrew-NG
- 机器学习的核心概念，由著名的专家Andrew NG编写，适合初学者学习。-machine learning key points by Andrew NG，which is the notes of the machine learning lecture.
machine-learning-yearning-andrew-ng
- 机器学习 andrew ng,Machine learning is the foundation of countless important applications, including web search, email anti-spam, speech recognition, product recommendations, and more. I assume that you or your team is working on a machine learning a
Andrew Ng
- Andrew Ng机器学习视频配套matlab代码(Andrew Ng machine learning video matching matlab code)
stanford_dl_ex-master
- coursera斯坦福Andrew Ng的深度学习编程作业答案(This is the answer of Andrew ng's deep learning curriculum in coursera.)
MachineLearning_Andrew Ng 2
- machine learning Andrew Ng
ipython-notebooks-master
- 机器学习详细课程代码以及讲解， Andrew Ng 机器学习课程练习的实现(Andrew Ng's "Machine Learning")
吴恩达深度学习基础教程
- 吴恩达教授有关深度学习浅显易懂的算法介绍，包括基础算法和示例。(Deep learning algorithms introduction by Andrew Ng.)
Andrew Ng machine-learning-ex4
- 吴恩达机器学习课程源码，第4个练习作业代码(Andrew machine learning course source code, the fourth practice code)