Tabla de Contenidos: “…. -- How it works... -- Using PageRank to determine airport ranking -- Getting ready -- How to do it... -- How it works... -- Finding the fewest number of connections -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Visualizing the graph -- Getting ready -- How to do it... -- How it works... -- Index…”

Tabla de Contenidos: “…第7 章 级大数据分析 -- 7.1 介绍 -- 7.2 使用Apache Giraph 算PageRank -- 7.3 使用Apache Giraph 算单源最短 径 -- 7.4 使用Apache Giraph 执 分布式宽度优先搜索 -- 7.5 使用Apache Mahout 算协同 滤 -- 7.6 使用Apache Mahout 类 -- 7.7 使用Apache Mahout 情感分类 -- 第8 章 -- 8.1 介绍 -- 8.2 在MapReduce 中使用Counters 监测异常 录 -- 8.3 使用MRUnit 开发和测 MapReduce -- 8.4 本地模式下开发和测 MapReduce -- 8.5 MapReduce 作业 异常 录 -- 8.6 在流 算作业中使用Counters -- 8.7 更改任务状态显示 信息 -- 8.8 使用illustrate Pig 作业 -- 第9 章系统管理 -- 9.1 介绍 -- 9.2 在伪分布模式下启动Hadoop -- 9.3 在分布式模式下启动Hadoop -- 9.4 添加一个新 点 -- 9.5 点安全 役 -- 9.6 NameNode 故 恢复 -- 9.7 使用Ganglia 监控 群 -- 9.8 MapReduce 作业参数 优 -- 第10 章使用Apache Accumulo 持久化 -- 10.1 介绍 -- 10.2 在Accumulo 中 存储地理事件 -- 10.3 使用MapReduce 批 导入地理事件数据到Accumulo -- 10.4 置 定义字段约束Accumulo 中的地理事件数据 -- 10.5 使用正则 滤器 制查 结果 -- 10.6 使用SumCombiner 算同一个 的不同版本的死亡数总和 -- 10.7 使用Accumulo 实 单元级安全的扫描 -- 10.8 使用MapReduce Accumulo 中的消息源 -- 欢 来到异步社区 -- 封底…”

Tabla de Contenidos: “…Inhaltsverzeichnis 7 -- Tabellenverzeichnis 9 -- Abbildungsverzeichnis 12 -- Einführung 15 -- 1.1 Überlegungen zu lnfrastrukturleistungen 15 -- 1.2 Überlegungen zu Kollaborationsplattformen . 17 -- 1.3 Aufbau der Arbeit 21 -- Volkswirtschaftstheoretische Betrachtung . 23 -- 2.1 Wissen als ökonomische Kategorie 24 -- 2.2 Transparenz vs. lntransparenz . 27 -- 2.3 Ökonomie als evolutionärer Prozess 31 -- 2.4 Entropie 35 -- 2.5 Kontextualisierung und Bedeutungskoordination . 37 -- Überlegungen zu einem Transaktionssystemunterstützungsmodell. . 41 -- 3.1 Input-Output-Tabellen als produktionsbezogene Transaktionsdarstellung 47 -- 3.2 Zum Aufbau der Input-Output-Rechnung . 49 -- 3.2.1 Zuordnungsprinzipien und Aufstellungsprobleme 57 -- 3.2.2 Annahmen bei der Aufstellung von Input-Output-Tabellen 59 -- 3.2.3 Generierung von Güter x Güter IOT mit der Gütertechnologieannahme . 62 -- 3.2.4 Negative Werte im Zuge der Gütertechnologieannahme . 68 -- 3.3 Alternative Formen der Produktionsstrukturerhebung 73 -- BCI - Modell und Daten . 75 -- Datenaufbereitungsphase 83 -- 5.1 Profildaten . 83 -- 5.2 Transaktionsdaten 84 -- 5.3 Strukturdaten 86 -- 5.4 Abgeleitete Datenquellen 87 -- 5.4.1 ,Seil' -Matrizen . 88 -- 5.4.2 ,Buy'-Matrizen 91 -- 5.4.3 Erweiterungen der ,Buy'-Matrizen 93 -- 5.4.4 BCliot - Input-Output-Relationen auf der -- Kollaborationsplattform 97 -- Strukturanalysephase 101 -- 6.1 Graphentheoretische Grundlagen 101 -- 6.2 Einfache Verflechtungseigenschaften auf Basis der Graphentheorie 107 -- 6.2.1 Grade und Dichte . 108 -- 6.2.2 Erreichbarkeit und Distanzen . 11 O -- 6.2.3 Indexierte Knoteneigenschaften . 112 -- 6.3 Knotenbetrachtung unter Einbezug direkter und indirekter -- Verflechtungen 116 -- 6.4 Berücksichtigung von Kantengewichten bei der Bewertung von Knoten 121 -- 6.4.1 Bedeutungsbestimmung mit der Leontief-lnverse 122 -- 6.4.2 Cliquenidentifikation und komplementärer Status nach Hubbell" 126 -- 6.4.3 Zwischen Perron-Frobenius und PageRankTM . 132 -- 6.5 Darstellung von Verflechtungsdaten . 135 -- 6.6 Blockmodeling - Clustering und Partitionierung von relationalen Datensätzen 137 -- 6.6.1 Direkte und indirekte Verfahren . 140 -- 6.6.2 Äquivalenzrelationen und Blocktypen 141 -- 6.6.3 Optimierungsprozess . 145 -- 6.6.4 Two-mode Blockmodeling 148 -- 6.6.5 Illustration des two-mode Blockmodeling . 150 -- 6.6.6 Blockmodellberechnung für gewichtete Relationen . 153 -- 6.6.7 Illustration der Blockmodellberechnung für gewichtete Kanten 156 -- 7 Strukturvergleichsphase 159 -- 7 .1 Lückentypologie 159 -- 7.2 Identifikation von direkten internen Lücken & potenziellen -- Performanzlücken . 162 -- 7.3 Identifikation von indirekten internen und externen Lücken 166 -- 7.4 Identifikation von internen Lückenfolgen 169 -- 7.5 Identifikation von externen Lückenfolgen . 173 -- 7.6 Deskriptive Auswertung der Lücken bzw. …”

Tabla de Contenidos: “…6.5 C4.5 Algorithm: Generating Decision Rules 185 -- 6.6 CART Algorithm & Gini Index 189 -- 6.7 Limitations of Decision Trees and Decision Rules 192 -- 6.8 Review Questions and Problems 194 -- 6.9 References for Further Study 198 -- 7 ARTIFICIAL NEURAL NETWORKS 199 -- 7.1 Model of an Artifi cial Neuron 201 -- 7.2 Architectures of ANNs 205 -- 7.3 Learning Process 207 -- 7.4 Learning Tasks Using ANNs 210 -- 7.5 Multilayer Perceptrons (MLPs) 213 -- 7.6 Competitive Networks and Competitive Learning 221 -- 7.7 SOMs 225 -- 7.8 Review Questions and Problems 231 -- 7.9 References for Further Study 233 -- 8 ENSEMBLE LEARNING 235 -- 8.1 Ensemble-Learning Methodologies 236 -- 8.2 Combination Schemes for Multiple Learners 240 -- 8.3 Bagging and Boosting 241 -- 8.4 AdaBoost 243 -- 8.5 Review Questions and Problems 245 -- 8.6 References for Further Study 247 -- 9 CLUSTER ANALYSIS 249 -- 9.1 Clustering Concepts 250 -- 9.2 Similarity Measures 253 -- 9.3 Agglomerative Hierarchical Clustering 259 -- 9.4 Partitional Clustering 263 -- 9.5 Incremental Clustering 266 -- 9.6 DBSCAN Algorithm 270 -- 9.7 BIRCH Algorithm 272 -- 9.8 Clustering Validation 275 -- 9.9 Review Questions and Problems 275 -- 9.10 References for Further Study 279 -- 10 ASSOCIATION RULES 280 -- 10.1 Market-Basket Analysis 281 -- 10.2 Algorithm Apriori 283 -- 10.3 From Frequent Itemsets to Association Rules 285 -- 10.4 Improving the Effi ciency of the Apriori Algorithm 286 -- 10.5 FP Growth Method 288 -- 10.6 Associative-Classifi cation Method 290 -- 10.7 Multidimensional Association-Rules Mining 293 -- 10.8 Review Questions and Problems 295 -- 10.9 References for Further Study 298 -- 11 WEB MINING AND TEXT MINING 300 -- 11.1 Web Mining 300 -- 11.2 Web Content, Structure, and Usage Mining 302 -- 11.3 HITS and LOGSOM Algorithms 305 -- 11.4 Mining Path-Traversal Patterns 310 -- 11.5 PageRank Algorithm 313 -- 11.6 Text Mining 316 -- 11.7 Latent Semantic Analysis (LSA) 320 -- 11.8 Review Questions and Problems 324 -- 11.9 References for Further Study 326.…”

“…Note: Per the Penguin Policy 2.0 update, some of the tasks in Chapter 6 may present a risk to Google page rank. Please read the latest policy update from Google to know fully what will work best for increasing and maintaining Google Page Rank…”

“…He then presents larger-scale applications: PageRank, Google's founding algorithm; and neural networks and deep learning. …”

“…By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. …”

“…Your exploration begins by learning about three different categories of algorithms, including within them the world-famous PageRank algorithm, and going through some use cases that are particularly well suited for graph algorithms. …”

“…This book reveals 51 PROVEN SEARCH ENGINE OPTIMIZATION TECHNIQUES and bite-size, easy-to-use advice that gets results including The truth about page rankings The truth about best SEO practices and SEO no-no’s The truth about link love, keywords, and tags Introduction vii Foreword by Fredrick Marckini ix Part I: The Basics of Search Truth 1: Getting noticed by spiders, robots, and crawlers 1 Truth 2: Learn to do the Google dance 5 Truth 3: It's not about traffic–it's about qualified traffic 9 Truth 4: Your reputation is on the line 13 Part II: The Truth About Being Site-Specific Truth 5: SEO is an ongoing project, not set-it-and-forget-it 17 Truth 6: SEO is not an afterthought 21 Truth 7: SEO results aren't immediate or lasting 25 Truth 8: You don't have a homepage anymore 29 Truth 9: Think like a publisher, even if you're not 33 Truth 10: Site and page design count 37 Truth 11: Write for users and search engines will follow 41 Truth 12: Keywords are key 45 Truth 13: Use analytics and keyword research tools 49 Truth 14: Site stats share the bad news, too 53 Truth 15: Think twice about hot new technologies 57 Truth 16: Content manageme..…”

“…As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. …”

“…As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. …”

“…By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. …”

“…By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. …”

“…As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. …”

“…Desde que en 1996 Larry Page y Sergei Brin desarrollaron uno de los algoritmos más famosos y mejor guardados del mundo, el Page Rank(TM), Google ha consolidado su carácter de gran empresa hasta convertirse en el principal aspirante al monopolio de la información en la era digital. …”

“…Per una costat, SETAL ordena tots els continguts per paraules, oracions, textos i fins i tot usuaris, segons la seva ordre d'importància, elaborat per la coocurrencia⁠ de paraules clau, les propietats de la Teoria de Grafs i de la seva distribució estadística (similar a l'algoritme utilitzat pel motor de cerca de Google per determinar el PageRank dels llocs web, a partir d'una cadena de Markov). …”
Accés lliure

“…Lessons Covered Include: Section 1: Rust Data Structures: Collections Lesson 1: Getting Started With The Modern Rust Development Ecosystem Meet the instructor & Course Overview: 1.0-meet-instructor-course-overview.mp4 Introduction to the AI Coding Paradigm Shift: 1.1-ai-pair-programming-paradigm-shift.mp4 Introduction to cloud-based development environments: 1.2-GitHub-Codespaces-Ecosystem-with-copilot-chat.mp4 Introduction to GitHub Copilot Ecosystem for Rust: 1.3-copilot-enabled-rust.mp4 Prompt Engineering with GCP BigQuery SQL: 1.4-big-query-prompt-engineering.mp4 Introduction to AWS CodeWhisperer for Rust: 1.5-aws-codewhisperer-for-rust.mp4 Using Google Bard to Enhance Productivity: 1.6-using-bard-to-enhance-productivity.mp4 Continuous Integration with Rust and GitHub Actions: 1.7-continuous-integration-rust-github-actions.mp4 Lesson 2: Rust Sequences and Maps Introducing Rust Sequences and Maps: 1.8-rust-sequences-maps.mp4 Print Rust data structures demo: 1.9-Print-Rust-data-structures-demo.mp4 Vector Fruit Salad demo: 1.10-Vector-Fruit-Salad-demo.mp4 VecDeque Fruit Salad demo: 1.11-VecDeque-Fruit-Salad-demo.mp4 Linkedin List Fruit Salad demo: 1.12-Linkedin-List-Fruit-Salad-demo.mp4 Fruit Salad CLI demo: 1.13-Fruit-Salad-CLI-demo.mp4 HashMap frequency counter demo: 1.14-HashMap-frequency-counter-demo.mp4 HashMap language comparison: 1.15-HashMap-language-comparison.mp4 Lesson 3: Rust Sets, Graphs and Miscellaneous Data Structures Analyzing UFC Fighter Network Using Graph Centrality in Rust: 1.16-ufc-graph-centrality.mp4 Storing Unique Fruits Using HashSet in Rust: 1.17-unique-fruits-with-hashset.mp4 Maintaining Sorted and Unique Fruits Using BTreeSet in Rust: 1.18-sorted-unique-fruits-with-btreeset.mp4 Creating a Fig Priority Fruit Salad Using Binary Heap in Rust: 1.19-fig-priority-fruit-salad-with-binary-heap.mp4 PageRank algorithm for sports data: 1.20-pagerank-sports.mp4 Showing shortest path with dijkstra: 1.21-shortest-path.mp4 Detecting Strongly Connected Components: A Deep Dive into Kosaraju's Algorithm: 1.22-strongly_connected_components_with_kosaraju.mp4 Simple Charting of Data Structures in Rust: 1.23-ascii-graphing.mp4 Section 2: Safety, Security and Concurrency with Rust Lesson 1: Rust Safety and Security Features Multi-Factor Authentication: 2.1_Multi-Factor_Authentication.mp4 Network Segmentation: 2.2_Network_Segmentation.mp4 Least Privilege Access: 2.3_Least_Privilege_Access.mp4 Encryption: 2.4_Encryption.mp4 Mutable fruit salad: 2.5-mutable-fruit-salad.mp4 Customize fruit salad with a CLI: 2.6-customize-csv-fruit-salad.mp4 Data Race example: 2.7-data-race.mp4 Lesson 2: Security Programming with Rust High Availability: 2.10-high-availability.mp4 Understanding the Homophonic Cipher: A Cryptographic Technique: 2.11-homphonic-cipher.mp4 Decoding the Secrets of the Caesar Cipher: 2.12-caesar-cipher.mp4 Building a Caesar Cipher Command Line Interface: 2.13-caesar-cipher-cli.mp4 Creating a Decoder Ring: A Practical Guide: 2.14-decoder-ring.mp4 Detecting Duplicates with SHA-3: A Data Integrity Tool: 2.15-sha3-dupe-detector.mp4 Incident Response: 2.16-incident-response.mp4 Compliance: 2.17-compliance.mp4 Lesson 3: Concurrency with Rust Core Concepts in Concurrency: 2.20-core-concepts-concurrency.mp4 Dining Philosophers: 2.21-dining-philosopher.mp4 Web Crawl Wikipedia with Rayon: 2.22-web-crawl-wikipedia-rayon.mp4 Intelligent Chatbot with Tokio: 2.23-tokio-chatbot.mp4 Multi-threaded deduplication with Rust: 2.24-data-eng-with-rust-dedupe.mp4 Energy Efficiency Python vs Rust: 2.25-energy-efficiency-python-rust.mp4 Concurrency Stress test with a GPU: 2.26-building-cuda-enabled-stress-test-with-rust-pytorch.mp4 Host Efficiency Serverless Optimization problem: 2.27-host-efficiency-optimization-problem.mp4 Section 3: Rust Data Engineering Libraries and Tools Lesson 1: Using Rust to Manage Data, Files and Network Storage Process CSV files in Rust: 2.31-process-csv-rust.mp4 Using Cargo Lambda with Rust: 2.33-cargo-lambda-rust.mp4 List files on AWS EFS with Rust: 2.34-rust-efs-lister.mp4 Use AWS S3 Storage: 2.35-use-s3-storage.mp4 Use AWS S3 Storage from Rust: 2.36-use-rust-for-s3-storage.mp4 Write encrypted data to tables or Parquet files: 2.37-Write-encrypted-data-to-tables-or-Parquet-files.mp4 Lesson 2: DataFrames with Rust, Python and Notebooks What is Colab?…”