舉報(bào) 
會(huì)員
The Unsupervised Learning Workshop
最新章節(jié)：
9. Hotspot Analysis
DoyoufinditdifficulttounderstandhowpopularcompanieslikeWhatsAppandAmazonfindvaluableinsightsfromlargeamountsofunorganizeddata?TheUnsupervisedLearningWorkshopwillgiveyoutheconfidencetodealwithclutteredandunlabeleddatasets,usingunsupervisedalgorithmsinaneasyandinteractivemanner.Thebookstartsbyintroducingthemostpopularclusteringalgorithmsofunsupervisedlearning.You'llfindouthowhierarchicalclusteringdiffersfromk-means,alongwithunderstandinghowtoapplyDBSCANtohighlycomplexandnoisydata.Movingahead,you'lluseautoencodersforefficientdataencoding.Asyouprogress,you’lluset-SNEmodelstoextracthigh-dimensionalinformationintoalowerdimensionforbettervisualization,inadditiontoworkingwithtopicmodelingforimplementingnaturallanguageprocessing(NLP).Inlaterchapters,you’llfindkeyrelationshipsbetweencustomersandbusinessesusingMarketBasketAnalysis,beforegoingontouseHotspotAnalysisforestimatingthepopulationdensityofanarea.Bytheendofthisbook,you’llbeequippedwiththeskillsyouneedtoapplyunsupervisedalgorithmsoncluttereddatasetstofindusefulpatternsandinsights.
目錄(72章)
倒序
- 封面
- 版權(quán)信息
- Preface
- 1. Introduction to Clustering
- Introduction
- Unsupervised Learning versus Supervised Learning
- Clustering
- Introduction to k-means Clustering
- Summary
- 2. Hierarchical Clustering
- Introduction
- Clustering Refresher
- The Organization of the Hierarchy
- Introduction to Hierarchical Clustering
- Linkage
- Agglomerative versus Divisive Clustering
- k-means versus Hierarchical Clustering
- Summary
- 3. Neighborhood Approaches and DBSCAN
- Introduction
- Clusters as Neighborhoods
- Introduction to DBSCAN
- DBSCAN versus k-means and Hierarchical Clustering
- Summary
- 4. Dimensionality Reduction Techniques and PCA
- Introduction
- What Is Dimensionality Reduction?
- Overview of Dimensionality Reduction Techniques
- Principal Component Analysis
- Summary
- 5. Autoencoders
- Introduction
- Fundamentals of Artificial Neural Networks
- Autoencoders
- Summary
- 6. t-Distributed Stochastic Neighbor Embedding
- Introduction
- The MNIST Dataset
- Stochastic Neighbor Embedding (SNE)
- t-Distributed SNE
- Interpreting t-SNE Plots
- Summary
- 7. Topic Modeling
- Introduction
- Topic Models
- Cleaning Text Data
- Latent Dirichlet Allocation
- Non-Negative Matrix Factorization
- Summary
- 8. Market Basket Analysis
- Introduction
- Market Basket Analysis
- Characteristics of Transaction Data
- The Apriori Algorithm
- Association Rules
- Summary
- 9. Hotspot Analysis
- Introduction
- Spatial Statistics
- Kernel Density Estimation
- Hotspot Analysis
- Summary
- Appendix
- 1. Introduction to Clustering
- 2. Hierarchical Clustering
- 3. Neighborhood Approaches and DBSCAN
- 4. Dimensionality Reduction Techniques and PCA
- 5. Autoencoders
- 6. t-Distributed Stochastic Neighbor Embedding
- 7. Topic Modeling
- 8. Market Basket Analysis
- 9. Hotspot Analysis 更新時(shí)間：2021-06-18 18:13:09
推薦閱讀
- 顯卡維修知識(shí)精解
- 電腦維護(hù)與故障排除傻瓜書（Windows 10適用）
- Python GUI Programming：A Complete Reference Guide
- 電腦組裝與維修從入門到精通（第2版）
- 計(jì)算機(jī)組裝·維護(hù)與故障排除
- 嵌入式系統(tǒng)設(shè)計(jì)教程
- scikit-learn：Machine Learning Simplified
- Spring Cloud微服務(wù)架構(gòu)實(shí)戰(zhàn)
- 筆記本電腦使用、維護(hù)與故障排除從入門到精通（第5版）
- Blender Quick Start Guide
- 龍芯自主可信計(jì)算及應(yīng)用
- Neural Network Programming with Java（Second Edition）
- 新編電腦組裝與硬件維修從入門到精通
- Spring Cloud實(shí)戰(zhàn)
- FreeSWITCH Cookbook
- Istio實(shí)戰(zhàn)指南
- 分布式存儲(chǔ)系統(tǒng)：核心技術(shù)、系統(tǒng)實(shí)現(xiàn)與Go項(xiàng)目實(shí)戰(zhàn)
- 施耐德M241/251可編程序控制器應(yīng)用技術(shù)
- Exceptional C++：47個(gè)C++工程難題、編程問題和解決方案（中文版）
- USB 3.0編程寶典
- GLSL Essentials
- GateIn Cookbook
- Mastering Adobe Photoshop Elements 2020
- Arduino圖形化編程進(jìn)階實(shí)戰(zhàn)
- Mastering Lumion 3D
- Apache Kylin權(quán)威指南（第2版）
- 計(jì)算機(jī)組裝與維護(hù)
- 辦公自動(dòng)化高級(jí)應(yīng)用案例教程
- KVM實(shí)戰(zhàn)：原理、進(jìn)階與性能調(diào)優(yōu)
- 單片機(jī)從入門到實(shí)戰(zhàn)（視頻自學(xué)版）
