搜索资源列表
ESL
- 机器学习经典书籍，迈向高手必读的进阶类书籍。书中包涵大量理论知识及数学推导（尤其是习题），有助于了解机器学习各种方法背后的本质思想。(Machine learning classic books, advanced books must go to the master. The book contains a large number of theoretical knowledge and mathematical derivation (especially the exercises),
scikit-learn-docs
- 学习机器学习的利器，非常适合初学者，书写的十分详细(Learning machine learning tool)
svmMLiA
- svm的简单实现，代码源自《机器学习实战》(a simple model of SVM)
coursera作业答案 仅供参考
- 机器学习 coursera上第一章，关于linear regression部分的知识和练习答案，供参考(the chapter one of machine learning on the coursera.It is about the knowledge of linear regression and the exercise1.)
handson-ml-master
- 这个项目的目的是教你机器学习的基本原理。它包含了Hands-on Machine Learning with Scikit-Learn and TensorFlow这本书的示例代码和解决方案。非常好的一本书！(This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in
模式识别与机器学习3贝叶斯决策理论
- 讲述模式识别与机器学习,贝叶斯决策理论的相关知识,(Bayesian decision theory in narrative pattern recognition)
感知器算法证明
- 机器学习中神经网络的感知器算法的相关证明(The proof of perceptron algorithm of neural network in machine learning)
人工神经网络后向传播算法推导
- 机器学习中神经网络的后向传播算法的相关推导(The derivation of backpropagation algorithm for neural networks in machine learning)
NEURAL_NETWORK_CODE
- 机器学习中神经网络的NN网络和CNN网络的MATLAB程序例程(NN neural network in machine learning and CNN network MATLAB routines)
Machine learning an algorithmic perspective
- 机器学习(Machine Learning, ML)是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能(Machine learning (Machine Learning ML) is an interdisciplinary subject, involving probability and statistics, approximation th
1502700551325609
- 用python编写与图片相关的机器学习神经网络，分享一下(nothing but null nothing nothing nothing but null nothing nothing)
Pattern Recognition and Machine Learning
- 机器学习和图像识别，自有资源上传，不是原创(Machine learning and image recognition)
Deep-learning
- 深度学习与机器学习经典之作，欢迎大家下载越读~~~~(Deep learning and machine learning)
机器学习实战(Peter Harrington 著)
- K-近邻算法,机器学习第二章节部分第一个程序源码，python3.6可运行(K- nearest neighbor algorithm, machine learning section second chapter, the first program source code, python3.6 can run)
一天搞懂深度学校-李宏毅
- 机器学习初学者入门所用，能快速厘清相关概念(Machine learning beginners access, can quickly clarify the relevant concepts)
ex1
- 斯坦福大学机器学习课程练习一的参考源码，可以直接运行，辅助学习，仅供参考。(A reference source code of Stanford University machine learning course. It can run directly. Aided learning. For reference only.)
《神经网络与机器学习》-课件ppt-课后答案
- 第三版神经网络与机器学习答案 simon(Third edition answer)
Machine Learning in Python(2015)
- 机器学习Python教程，见解精髓，适合入门和进阶用，需要较好的编程和数学基础(Machine Learning in Python(2015))
机器学习课程2014源代码
- python数据分析，人工智能，吴恩达课程代码(Python data analysis, artificial intelligence, teacher Wu Enda curriculum code)
PRML
- Christopher M Bishop，PRML 机器学习的经典书籍(A masterpiece of Machine Learning written by Christopher M Bishop, PRML.)