决策树分类matlab代码应用机器学习和数据科学食谱-面向初学者的数据科学编码训练营 使用Python,R和MATLAB的应用机器学习和数据科学 适用于应用机器学习和数据科学的Python,R和MATLAB代码列表 应用机器学习和数据科学的7个步骤: 通过编码分类学习: 分类: 数据分析: 数据科学: 数据可视化: 机器学习食谱: 熊猫: Python: SKLEARN: 监督学习: 表格数据分析: 端到端数据科学食谱: 应用统计: 套袋乐团: 促进合奏: CatBoost: 聚类: 数据分析: 数据科学: 数据可视化: 决策树: LightGBM: 机器学习食谱: 多类别分类: 神经网络: Python机器学习: Python机器学习速成课程: R分类: R对于初学者: R for Business Analytics: R for Data Science: 用于数据可视化的R: 适用于Excel用户的R: R机器学习: R机器学习速成课程: R回归: 回归: XGBOOST: 有抱负的数据科学家的项目组合项目:表格文本和图像数据分析以及Python和R @中的时间序列预测 西澳大
2021-10-19 16:49:27 1KB 系统开源
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关于统计推理的电子书,最新版本,经典著作,总页数为381页
2021-10-14 21:07:24 7.55MB Statistical Inference
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经典之作,收藏
2021-10-10 20:50:14 31.73MB 应用泛函分析
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APPLIED NUMERICAL LINEAR ALGEBRA James W. Demmel University of California Berkeley, California Society for Industrial and Applied Mathematics Philadelphia Contents Preface ix 1 Introduction 1 1.1 Basic Notation 1 1.2 Standard Problems of Numerical Linear Algebra 1 1.3 General Techniques 2 1.3.1 Matrix Factorizations 3 1.3.2 Perturbation Theory and Condition Numbers 4 1.3.3 Effects of Roundoff Error on Algorithms 5 1.3.4 Analyzing the Speed of Algorithms 5 1.3.5 Engineering Numerical Software 6 1.4 Example: Polynomial Evaluation 7 1.5 Floating Point Arithmetic 9 1.5.1 Further Details 12 1.6 Polynomial Evaluation Revisited 15 1.7 Vector and Matrix Norms 19 1.8 References and Other Topics for Chapter 1 23 1.9 Questions for Chapter 1 24 2 Linear Equation Solving 31 2.1 Introduction 31 2.2 Perturbation Theory 32 2.2.1 Relative Perturbation Theory 35 2.3 Gaussian Elimination 38 2.4 Error Analysis 44 2.4.1 The Need for Pivoting 45 2.4.2 Formal Error Analysis of Gaussian Elimination 46 2.4.3 Estimating Condition Numbers 50 2.4.4 Practical Error Bounds 54 2.5 Improving the Accuracy of a Solution 60 2.5.1 Single Precision Iterative Refinement 62 2.5.2 Equilibration 62 2.6 Blocking Algorithms for Higher Performance 63 2.6.1 Basic Linear Algebra Subroutines (BLAS) 66 2.6.2 How to Optimize Matrix Multiplication 67 2.6.3 Reorganizing Gaussian Elimination to Use Level 3 BLAS 72 2.6.4 More About Parallelism and Other Performance Issues . 75 vi Contents 2.7 2.8 2.9 Special Linear Systems 2.7.1 Real Symmetric Positive Definite Matrices 2.7.2 Symmetric Indefinite Matrices 2.7.3 Band Matrices 2.7.4 General Sparse Matrices 2.7.5 Dense Matrices Depending on Fewer Than O(n2) Pa- rameters References and Other Topics for Chapter 2 Questions for Chapter 2 76 76 79 79 83 90 93 93 3 Linear Least Squares Problems 101 3.1 Introduction 101 3.2 Matrix Factorizations That Solve the Linear Least Squares Prob- lem 105 3.2.1 Normal Equations 106 3.2.2 QR Decomposition 107 3.2.3 Singular Value Decompos
2021-10-10 20:43:28 2.64MB Applied Numerical Linear Algebra
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卡尔曼滤波经典书籍,Introduction To Random Signals And Applied Kalman Filtering,英文原版第3版
2021-10-10 14:36:59 48.82MB 卡尔曼滤波
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将简单的解释与大量的实际示例结合起来,提供了一种创新的线性代数教学方法。 不需要先验知识,它涵盖线性代数的各个方面-向量,矩阵和最小二乘
2021-10-09 19:41:47 125B 数学
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Introduction to Applied Linear Algebra(线性代数应用) Stephen Boyd Department of Electrical Engineering Stanford University Lieven Vandenberghe Department of Electrical and Computer Engineering University of California, Los Angeles
2021-10-02 15:15:16 6.82MB Introd Stephe 线性代数及其 线性代数
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【2018新书】应用线性代数与矩阵分析(Applied Linear Algebra and Matrix Analysis, 2ed)
2021-09-29 21:23:52 8.17MB 线性代数 矩阵分析 人工智能
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Applied_Logistic_Regression_3rd_2013 第3版,关于logistic regression比较详细的书
2021-09-27 17:37:12 5.54MB book
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感应电机智能控制,英文高清版本 ,Applied Intelligent Control of Induction Motor Drives
2021-09-22 15:14:58 19.64MB intelligent control induction motor
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