当前位置：

Scikit-Learn与TensorFlow机器学习实用指南

作者：Aurelien Geron著

出版社：东南大学出版社

出版年：2017-10-01

评分：5分

ISBN：9787564173715

所属分类：网络科技

书刊介绍

Scikit-Learn与TensorFlow机器学习实用指南内容简介

本书很好地介绍了利用神经网络解决问题的相关理论与实践。它涵盖了构建高效应用涉及的关键点以及理解新技术所需的背景知识。

Scikit-Learn与TensorFlow机器学习实用指南本书特色

TensorFlow是一个采用数据流图（data flow graphs），用于数值计算的开源软件库。节点（Nodes）在图中表示数学操作，图中的线（edges）则表示在节点间相互联系的多维数据数组，即张量（tensor）。它灵活的架构让你可以在多种平台上展开计算，例如台式计算机中的一个或多个CPU（或GPU），服务器，移动设备等等。本书讲述TensorFlow相关知识。

Scikit-Learn与TensorFlow机器学习实用指南目录

PrefacePart I.The Fundamentals of Machine Learning1. The Machine Learning LandscapeWhat Is Machine Learning Why Use Machine Learning Types of Machine Learning SystemsSupervised/Unsupervised LearningBatch and Online LearningInstance-Based Versus Model-Based LearningMain Challenges of Machine LearningInsufficient Quantity of Training DataNonrepresentative Training DataPoor-Quality DataIrrelevant FeaturesOverfitting the Training DataUnderfitting the Training Data tepping BackTesting and ValidatingExercises2. End-to-End Machine Learning ProjectWorking with Real DataLook at the Big PictureFrame the ProblemSelect a Performance MeasureCheck the AssumptionsGet the DataCreate the WorkspaceDownload the DataTake a Quick Look at the Data StructureCreate a Test SetDiscover and Visualize the Data to Gain InsightsVisualizing Geographical DataLooking for CorrelationsExperimenting with Attribute CombinationsPrepare the Data for Machine Learning AlgorithmsData CleaningHandling Text and Categorical AttributesCustom TransformersFeature ScalingTransformation PipelinesSelect and Train a ModelTraining and Evaluating on the Training SetBetter Evaluation Using Cross-ValidationFine-Tune Your ModelGrid SearchRandomized SearchEnsemble MethodsAnalyze the Best Models and Their ErrorsEvaluate Your System on the Test SetLaunch, Monitor, and Maintain Your SystemTry It Out!Exercises3. ClassificationMNISTTraining a Binary ClassifierPerformance MeasuresMeasuring Accuracy Using Cross-ValidationConfusion MatrixPrecision and RecallPrecision/Recall TradeoffThe ROC CurveMulticlass ClassificationError AnalysisMultilabel ClassificationMultioutput Classification……Part II.Neural Networks and Deep LearningA. Exercise SolutionsB. Machine Learning Project ChecklistC. SVM Dual ProblemD. AutodiffE. Other Popular ANN ArchitecturesIndex