MobileNets V3的Caffe实现caffe-mobilenet-v3简介这是MobileNetV3的个人Caffe实现。 有关详细信息，请阅读原始文章：搜索MobileNetV3。 如何使用Caffe需求（请参阅：Caffe安装说明）添加新的caffe层并重建caffe：RuiminChen / Caffe-MobileNetV2-ReLU6的yonghenglh6 / DepthwiseConvolution ReLU6层的Depthwise卷积层运行测试CPU：$ CAFFE_ROOT / build / tools /咖啡时间-model mobilenet_v3_large_1.0.prototxt GPU

需求预测在农业、电力、旅游、零售和制造企业等许多行业都具有重要意义，它在每个企业的决策中都起着至关重要的作用。 本文以机器学习为重点，调查了需求预测的各种最新方法。 机器学习技术分为三类，即时间序列分析、基于回归的方法和监督/无监督模型。 讨论了各种机器学习技术的优缺点，并比较了它们的性能指标。 对比掩盖了LSTM有一个显着的结果，但它的计算时间比任何其他方法都要高。 未来研究的另一个领域包括基于回归的方法、混合模型和集成模型。 本研究为读者提供了机器学习领域需求预测的概念。

关于这个就不多说了，原来一直用3.0的版本，昨天在网上DOWN了个5.5的版本，多了SUM,COUNT,MIN,MAX等统计功能，界面也做了美化。 收藏了。 里面有两个DEMO 分别是3.0和7.0版本的DEMO 大概都差不多，大家可以参考下。

Neural Networks And Learning Machine(神经网络与学习机)，神经网络经典名著

Yelp分析和评级预测 概述 Yelp是一个带有社交网络工具的区域目录平台和审阅网站。 该网站提供了针对本地企业（水疗中心，餐厅，百货公司，酒吧，本地本地服务，商店，汽车）的众包评论。 这有助于用户进行业务评级和评论。 通常，评论是由几百行左右的单词组成的简短文本，描述了各个方面的各种用户体验。 这为企业所有者提供了改进产品的机会，并使客户可以选择最佳的行业。 商业价值/分析目标 管理层可能没有足够的时间来进行每一次审核。 如果可以一目了然地向他们提供有价值的信息和见解，那将是非常有用和节省时间的。 不仅对于管理人员，而且对于试图了解更多餐厅信息并需要一些帮助来订购或选择餐厅的客户，也是如此。 毕竟，在当今世界，每个人都喜欢在做出决定之前先阅读评论和反馈。 在我们的项目中，我们使用自然语言处理和机器学习来实现这些业务和客户目标。 我们专注于情感分析，主题建模，数据分析和评级预测的分类。 数

Statistical foundations of machine learning

作者：詹姆斯·狄更斯（James Dickens），锡7118781 最终项目-CSI 5155：机器学习，Herna Viktor博士教授的课程。 这是我针对数据的二进制分类的机器学习任务的代码，该代码可从，该数据由从1994年提取的加权普查数据组成1995年由美国人口普查局进行的当前人口调查。 目标是评估五个常用的机器学习模型（包括半监督神经网络！），以对给定实例每年赚取超过50K进行分类，也就是二进制分类任务。 我的代码组织如下： Preprocess.py接收初始的census-income.data文件和census-income.test文件，然后 打印有关数据及其属性的信息 从训练数据中删除重复项 处理实例重量冲突 将缺失的值替换为其默认值 将结果写入文件：“ census-income.data/training_data_preprocess1”，“ census

Rex：一个开源的四足机器人 该项目的目标是训练一个开源3D打印四足机器人，探索Reinforcement Learning和OpenAI Gym 。 目的是让机器人学习模拟中的家务和一般任务，然后在不进行任何其他手动调整的情况下，在真实机器人上成功地传递知识（ Control Policies ）。 该项目的主要灵感来自波士顿动力公司所做的令人难以置信的工作。 相关资料库 一个CLI应用程序，用于引导和控制Rex运行经过训练的Control Policies 。 cloud-用于在云上训练Rex的CLI应用程序。 Rex-Gym：OpenAI Gym环境和工具 该存储库包含用于训练Rex的OpenAI Gym Environments集合，Rex URDF模型，学习代理实现（PPO）和一些脚本，以开始训练课程并可视化学习到的Control Polices 。 此CLI应用程序允许批量培训，策略重现和单个培训呈现的会话。 安装 创建一个Python 3.7虚拟环境，例如使用Anaconda conda create -n rex python=3.7 anaconda cond

machine_learning 一个Matlab应用，使用支持向量机对图片进行分类，其中图片是猫脸还是狗脸 ＃数据集 为了训练分类器，我使用了来自 张维伟，孙健和唐小鸥，“猫头检测-如何有效利用形状和纹理特征”，Proc。 欧洲Conf。 计算机视觉，第一卷。 4，第802-816页，2008年。 和来自的斯坦福狗数据集 ＃托多斯 由于猫或狗之间难以分辨，因此需要预先修剪脸部以获得最佳效果。 可能没有第三个值，然后搜索猫脸或狗脸的图片（分解成窗户）。 或者只是狗或不面对，等等。 #Cat注释数据集 ＃＃＃结构 |-- cat_dataset |-- CAT_00 |-- 00000001_000.jpg |-- 00000001_000.jpg.cat |-- 00000001_005.jpg |-- 000

1 Introduction 19 1.1 What Is Learning? 19 1.2 When Do We Need Machine Learning? 21 1.3 Types of Learning 22 1.4 Relations to Other Fields 24 1.5 How to Read This Book 25 1.5.1 Possible Course Plans Based on This Book 26 1.6 Notation 27 Part I Foundations 31 2 A Gentle Start 33 2.1 A Formal Model { The Statistical Learning Framework 33 2.2 Empirical Risk Minimization 35 2.2.1 Something May Go Wrong { Overtting 35 2.3 Empirical Risk Minimization with Inductive Bias 36 2.3.1 Finite Hypothesis Classes 37 2.4 Exercises 41 3 A Formal Learning Model 43 3.1 PAC Learning 43 3.2 A More General Learning Model 44 3.2.1 Releasing the Realizability Assumption { Agnostic PAC Learning 45 3.2.2 The Scope of Learning Problems Modeled 47 3.3 Summary 49 3.4 Bibliographic Remarks 50 3.5 Exercises 50 4 Learning via Uniform Convergence 54 4.1 Uniform Convergence Is Sucient for Learnability 54 4.2 Finite Classes Are Agnostic PAC Learnable 55 Understanding Machine Learning, c 2014 by Shai Shalev-Shwartz and Shai Ben-David Published 2014 by Cambridge University Press. Personal use only. Not for distribution. Do not post. Please link to http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning x Contents 4.3 Summary 58 4.4 Bibliographic Remarks 58 4.5 Exercises 58 5 The Bias-Complexity Tradeo 60 5.1 The No-Free-Lunch Theorem 61 5.1.1 No-Free-Lunch and Prior Knowledge 63 5.2 Error Decomposition 64 5.3 Summary 65 5.4 Bibliographic Remarks 66 5.5 Exercises 66 6 The VC-Dimension 67 6.1 Innite-Size Classes Can Be Learnable 67 6.2 The VC-Dimension 68 6.3 Examples 70 6.3.1 Threshold Functions 70 6.3.2 Intervals 71 6.3.3 Axis Aligned Rectangles 71 6.3.4 Finite Classes 72 6.3.5 VC-Dimension and the Number of Parameters 72 6.4 The Fundamental Theorem of PAC learning 72 6.5 Proof of Theorem 6.7 73 6.5.1 Sauer's Lemma and the Growth Function 73 6.5.2 Uniform Convergence for Classes of Small Eective Size 75 6.6 Summary 78 6.7 Bibliographic remarks 78 6.8 Exercises 78 7 Nonuniform Learnability 83 7.1 Nonuniform Learnability 83 7.1.1 Characterizing Nonuniform Learnability 84 7.2 Structural Risk Minimization 85 7.3 Minimum Description Length and Occam's Razor 89 7.3.1 Occam's Razor 91 7.4 Other Notions of Learnability { Consistency 92 7.5 Discussing the Dierent Notions of Learnability 93 7.5.1 The No-Free-Lunch Theorem Revisited 95 7.6 Summary 96 7.7 Bibliographic Remarks 97 7.8 Exercises 97 8 The Runtime of Learning 100 8.1 Computational Complexity of Learning 101 Contents xi 8.1.1 Formal Denition* 102 8.2 Implementing the ERM Rule 103 8.2.1 Finite Classes 104 8.2.2 Axis Aligned Rectangles 105 8.2.3 Boolean Conjunctions 106 8.2.4 Learning 3-Term DNF 107 8.3 Eciently Learnable, but Not by a Proper ERM 107 8.4 Hardness of Learning* 108 8.5 Summary 110 8.6 Bibliographic Remarks 110 8.7 Exercises 110 Part II From Theory to Algorithms 115 9 Linear Predictors 117 9.1 Halfspaces 118 9.1.1 Linear Programming for the Class of Halfspaces 119 9.1.2 Perceptron for Halfspaces 120 9.1.3 The VC Dimension of Halfspaces 122 9.2 Linear Regression 123 9.2.1 Least Squares 124 9.2.2 Linear Regression for Polynomial Regression Tasks 125 9.3 Logistic Regression 126 9.4 Summary 128 9.5 Bibliographic Remarks 128 9.6 Exercises 128 10 Boosting 130 10.1 Weak Learnability 131 10.1.1 Ecient Implementation of ERM for Decision Stumps 133 10.2 AdaBoost 134 10.3 Linear Combinations of Base Hypotheses 137 10.3.1 The VC-Dimension of L(B; T) 139 10.4 AdaBoost for Face Recognition 140 10.5 Summary 141 10.6 Bibliographic Remarks 141 10.7 Exercises 142 11 Model Selection and Validation 144 11.1 Model Selection Using SRM 145 11.2 Validation 146 11.2.1 Hold Out Set 146 11.2.2 Validation for Model Selection 147 11.2.3 The Model-Selection Curve 148 xii Contents 11.2.4 k-Fold Cross Validation 149 11.2.5 Train-Validation-Test Split 150 11.3 What to Do If Learning Fails 151 11.4 Summary 154 11.5 Exercises 154 12 Convex Learning Problems 156 12.1 Convexity, Lipschitzness, and Smoothness 156 12.1.1 Convexity 156 12.1.2 Lipschitzness 160 12.1.3 Smoothness 162 12.2 Convex Learning Problems 163 12.2.1 Learnability of Convex Learning Problems 164 12.2.2 Convex-Lipschitz/Smooth-Bounded Learning Problems 166 12.3 Surrogate Loss Functions 167 12.4 Summary 168 12.5 Bibliographic Remarks 169 12.6 Exercises 169 13 Regularization and Stability 171 13.1 Regularized Loss Minimization 171 13.1.1 Ridge Regression 172 13.2 Stable Rules Do Not Overt 173 13.3 Tikhonov Regularization as a Stabilizer 174 13.3.1 Lipschitz Loss 176 13.3.2 Smooth and Nonnegative Loss 177 13.4 Controlling the Fitting-Stability Tradeo 178 13.5 Summary 180 13.6 Bibliographic Remarks 180 13.7 Exercises 181 14 Stochastic Gradient Descent 184 14.1 Gradient Descent 185 14.1.1 Analysis of GD for Convex-Lipschitz Functions 186 14.2 Subgradients 188 14.2.1 Calculating Subgradients 189 14.2.2 Subgradients of Lipschitz Functions 190 14.2.3 Subgradient Descent 190 14.3 Stochastic Gradient Descent (SGD) 191 14.3.1 Analysis of SGD for Convex-Lipschitz-Bounded Functions 191 14.4 Variants 193 14.4.1 Adding a Projection Step 193 14.4.2 Variable Step Size 194 14.4.3 Other Averaging Techniques 195 Contents xiii 14.4.4 Strongly Convex Functions* 195 14.5 Learning with SGD 196 14.5.1 SGD for Risk Minimization 196 14.5.2 Analyzing SGD for Convex-Smooth Learning Problems 198 14.5.3 SGD for Regularized Loss Minimization 199 14.6 Summary 200 14.7 Bibliographic Remarks 200 14.8 Exercises 201 15 Support Vector Machines 202 15.1 Margin and Hard-SVM 202 15.1.1 The Homogenous Case 205 15.1.2 The Sample Complexity of Hard-SVM 205 15.2 Soft-SVM and Norm Regularization 206 15.2.1 The Sample Complexity of Soft-SVM 208 15.2.2 Margin and Norm-Based Bounds versus Dimension 208 15.2.3 The Ramp Loss* 209 15.3 Optimality Conditions and \Support Vectors"* 210 15.4 Duality* 211 15.5 Implementing Soft-SVM Using SGD 212 15.6 Summary 213 15.7 Bibliographic Remarks 213 15.8 Exercises 214 16 Kernel Methods 215 16.1 Embeddings into Feature Spaces 215 16.2 The Kernel Trick 217 16.2.1 Kernels as a Way to Express Prior Knowledge 221 16.2.2 Characterizing Kernel Functions* 222 16.3 Implementing Soft-SVM with Kernels 222 16.4 Summary 224 16.5 Bibliographic Remarks 225 16.6 Exercises 225 17 Multiclass, Ranking, and Complex Prediction Problems 227 17.1 One-versus-All and All-Pairs 227 17.2 Linear Multiclass Predictors 230 17.2.1 How to Construct 230 17.2.2 Cost-Sensitive Classication 232 17.2.3 ERM 232 17.2.4 Generalized Hinge Loss 233 17.2.5 Multiclass SVM and SGD 234 17.3 Structured Output Prediction 236 17.4 Ranking 238 xiv Contents 17.4.1 Linear Predictors for Ranking 240 17.5 Bipartite Ranking and Multivariate Performance Measures 243 17.5.1 Linear Predictors for Bipartite Ranking 245 17.6 Summary 247 17.7 Bibliographic Remarks 247 17.8 Exercises 248 18 Decision Trees 250 18.1 Sample Complexity 251 18.2 Decision Tree Algorithms 252 18.2.1 Implementations of the Gain Measure 253 18.2.2 Pruning 254 18.2.3 Threshold-Based Splitting Rules for Real-Valued Features 255 18.3 Random Forests 255 18.4 Summary 256 18.5 Bibliographic Remarks 256 18.6 Exercises 256 19 Nearest Neighbor 258 19.1 k Nearest Neighbors 258 19.2 Analysis 259 19.2.1 A Generalization Bound for the 1-NN Rule 260 19.2.2 The \Curse of Dimensionality" 263 19.3 Ecient Implementation* 264 19.4 Summary 264 19.5 Bibliographic Remarks 264 19.6 Exercises 265 20 Neural Networks 268 20.1 Feedforward Neural Networks 269 20.2 Learning Neural Networks 270 20.3 The Expressive Power of Neural Networks 271 20.3.1 Geometric Intuition 273 20.4 The Sample Complexity of Neural Networks 274 20.5 The Runtime of Learning Neural Networks 276 20.6 SGD and Backpropagation 277 20.7 Summary 281 20.8 Bibliographic Remarks 281 20.9 Exercises 282 Part III Additional Learning Models 285 21 Online Learning 287 21.1 Online Classication in the Realizable Case 288 Contents xv 21.1.1 Online Learnability 290 21.2 Online Classication in the Unrealizable Case 294 21.2.1 Weighted-Majority 295 21.3 Online Convex Optimization 300 21.4 The Online Perceptron Algorithm 301 21.5 Summary 304 21.6 Bibliographic Remarks 305 21.7 Exercises 305 22 Clustering 307 22.1 Linkage-Based Clustering Algorithms 310 22.2 k-Means and Other Cost Minimization Clusterings 311 22.2.1 The k-Means Algorithm 313 22.3 Spectral Clustering 315 22.3.1 Graph Cut 315 22.3.2 Graph Laplacian and Relaxed Graph Cuts 315 22.3.3 Unnormalized Spectral Clustering 317 22.4 Information Bottleneck* 317 22.5 A High Level View of Clustering 318 22.6 Summary 320 22.7 Bibliographic Remarks 320 22.8 Exercises 320 23 Dimensionality Reduction 323 23.1 Principal Component Analysis (PCA) 324 23.1.1 A More Ecient Solution for the Case d m 326 23.1.2 Implementation and Demonstration 326 23.2 Random Projections 329 23.3 Compressed Sensing 330 23.3.1 Proofs* 333 23.4 PCA or Compressed Sensing? 338 23.5 Summary 338 23.6 Bibliographic Remarks 339 23.7 Exercises 339 24 Generative Models 342 24.1 Maximum Likelihood Estimator 343 24.1.1 Maximum Likelihood Estimation for Continuous Random Variables 344 24.1.2 Maximum Likelihood and Empirical Risk Minimization 345 24.1.3 Generalization Analysis 345 24.2 Naive Bayes 347 24.3 Linear Discriminant Analysis 347 24.4 Latent Variables and the EM Algorithm 348 xvi Contents 24.4.1 EM as an Alternate Maximization Algorithm 350 24.4.2 EM for Mixture of Gaussians (Soft k-Means) 352 24.5 Bayesian Reasoning 353 24.6 Summary 355 24.7 Bibliographic Remarks 355 24.8 Exercises 356 25 Feature Selection and Generation 357 25.1 Feature Selection 358 25.1.1 Filters 359 25.1.2 Greedy Selection Approaches 360 25.1.3 Sparsity-Inducing Norms 363 25.2 Feature Manipulation and Normalization 365 25.2.1 Examples of Feature Transformations 367 25.3 Feature Learning 368 25.3.1 Dictionary Learning Using Auto-Encoders 368 25.4 Summary 370 25.5 Bibliographic Remarks 371 25.6 Exercises 371 Part IV Advanced Theory 373 26 Rademacher Complexities 375 26.1 The Rademacher Complexity 375 26.1.1 Rademacher Calculus 379 26.2 Rademacher Complexity of Linear Classes 382 26.3 Generalization Bounds for SVM 383 26.4 Generalization Bounds for Predictors with Low `1 Norm 386 26.5 Bibliographic Remarks 386 27 Covering Numbers 388 27.1 Covering 388 27.1.1 Properties 388 27.2 From Covering to Rademacher Complexity via Chaining 389 27.3 Bibliographic Remarks 391 28 Proof of the Fundamental Theorem of Learning Theory 392 28.1 The Upper Bound for the Agnostic Case 392 28.2 The Lower Bound for the Agnostic Case 393 28.2.1 Showing That m(; ) 0:5 log(1=(4))=2 393 28.2.2 Showing That m(; 1=8) 8d=2 395 28.3 The Upper Bound for the Realizable Case 398 28.3.1 From -Nets to PAC Learnability 401 Contents xvii 29 Multiclass Learnability 402 29.1 The Natarajan Dimension 402 29.2 The Multiclass Fundamental Theorem 403 29.2.1 On the Proof of Theorem 29.3 403 29.3 Calculating the Natarajan Dimension 404 29.3.1 One-versus-All Based Classes 404 29.3.2 General Multiclass-to-Binary Reductions 405 29.3.3 Linear Multiclass Predictors 405 29.4 On Good and Bad ERMs 406 29.5 Bibliographic Remarks 408 29.6 Exercises 409 30 Compression Bounds 410 30.1 Compression Bounds 410 30.2 Examples 412 30.2.1 Axis Aligned Rectangles 412 30.2.2 Halfspaces 412 30.2.3 Separating Polynomials 413 30.2.4 Separation with Margin 414 30.3 Bibliographic Remarks 414 31 PAC-Bayes 415 31.1 PAC-Bayes Bounds 415 31.2 Bibliographic Remarks 417 31.3 Exercises 417 Appendix A Technical Lemmas 419 Appendix B Measure Concentration 422 Appendix C Linear Algebra 430 Notes 435