资源列表
DeepLearning
- 此书是最新版的深度学习书籍，极具参考价值。(This book is the latest edition of the book of deep learning, which is of great reference value.)
adaptiveBasis
- 贝叶斯参数学习电饭锅电饭锅的辅导费规范地方(Bayesian parameter learning)
8sm
- 一个利用深度优先搜索完成的八数码问题，人工智能(A eight digital problem with depth first search)
3-6
- 使用f[i][j]代表从i到j所花费最小费用，则将从i到j路径变成i->z->j，k代表要走的站点数量，起始位置为i所以终点位置为：i+k，保证最后一段为k所以循环截止到i<=n-k，z就是中间站点。如果这样比f[i][j]小，则交换值(Using f[i][j] to represent the minimum cost from I to j will turn I from J to i->z->j, K represents the number of si
3-12
- 双调旅行售货员问题 问题分析：给定平面上n个点，p[i]=(x[i],y[i])，I=1,2,3,…,n。点集p1,p2..pi按x坐标排序 t(i)表示点{p1,p2...pi}的最短双调TSP回路，则有， t(i) = min{t(k) + D(k,i) + d(k-1,i) - d(k-1,k)} ，1<k<i t(1) = 0，t(1) = 2d(1,2) d(i,j)为pi,pj之间的距离 D(i,j)为pi,pi+1,pi+1...pj之间累加距离 设s(i
3-17
- 字符串比较问题 问题分析：解答此题需要一个较为巧妙的解题思路。解决此题可以借用“最长公共子串”问题的解题思路。采用自底向上的动态规划思想。假设对于给定的字符串A，B长度分别为m,n，A[1..m],B[1..n],这里可以使用变量val[m][n]表示A,B的扩展距离。 对于字符串A[1..m],B[1..n],有以下两种情况： 1.A[m]和B[n]处在扩展字符串的同一个位置，那么val[m][n]=val[m-1][n-1]+abs(A[m]-B[n])。 2.A[m]和B[n]不在
神经网络与深度学习讲义
- 邱锡鹏老师的神经网络讲义，对想进入深度学习领域的同学很有帮助(Qiu Xipeng's neural network lecture is very helpful for students who want to get into the field of deep study)
bayes
- 基于贝叶斯理论的分类方法，过滤垃圾，从个人广告中获取区域倾向(Classification method based on Bayesian theory, Filter out rubbish, obtain the regional tendency from the personal advertisement)
Intro to Machine Learning.pdf
- INTRODUCTION TO MACHINE LEARNING Alex Smola and S.V.N. Vishwanathan
Machine Learning for Absolute Beginners.pdf
- Machine Learning for Absolute Beginners by Oliver Theobald
EvolutionaryComputation
- Evolutionary Computation Book
2012.李航.统计学习方法
- 机器学习算法，介绍各种机器学习算法，理论公式推导等等(Machine Learning Algorithms, Introduction to Various Machine Learning Algorithms, Derivation of Theoretical Formulas, and More)