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Machine Learning with Apache Spark Quick Start Guide : Uncover Patterns, Derive Actionable Insights, and Learn from Big Data Using MLlib.
Chapter 3: Artificial Intelligence and Machine Learning; Artificial intelligence; Machine learning; Supervised learning; Unsupervised learning; Reinforced learning; Deep learning; Natural neuron; Artificial neuron; Weights; Activation function; Heaviside step function; Sigmoid function; Hyperbolic t...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Quddus, Jillur. | |
| 245 | 1 | 0 | |a Machine Learning with Apache Spark Quick Start Guide : |b Uncover Patterns, Derive Actionable Insights, and Learn from Big Data Using MLlib. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (233 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter 1: The Big Data Ecosystem; A brief history of data; Vertical scaling; Master/slave architecture; Sharding; Data processing and analysis; Data becomes big; Big data ecosystem; Horizontal scaling; Distributed systems; Distributed data stores; Distributed filesystems; Distributed databases; NoSQL databases; Document databases; Columnar databases; Key-value databases; Graph databases; CAP theorem; Distributed search engines; Distributed processing; MapReduce; Apache Spark | |
| 505 | 8 | |a RDDs, DataFrames, and datasetsRDDs; DataFrames; Datasets; Jobs, stages, and tasks; Job; Stage; Tasks; Distributed messaging; Distributed streaming; Distributed ledgers; Artificial intelligence and machine learning; Cloud computing platforms; Data insights platform; Reference logical architecture; Data sources layer; Ingestion layer; Persistent data storage layer; Data processing layer; Serving data storage layer; Data intelligence layer; Unified access layer; Data insights and reporting layer; Platform governance, management, and administration; Open source implementation; Summary | |
| 505 | 8 | |a Chapter 2: Setting Up a Local Development EnvironmentCentOS Linux 7 virtual machine; Java SE Development Kit 8; Scala 2.11; Anaconda 5 with Python 3; Basic conda commands; Additional Python packages; Jupyter Notebook; Starting Jupyter Notebook; Troubleshooting Jupyter Notebook; Apache Spark 2.3; Spark binaries; Local working directories; Spark configuration; Spark properties; Environmental variables; Standalone master server; Spark worker node; PySpark and Jupyter Notebook; Apache Kafka 2.0; Kafka binaries; Local working directories; Kafka configuration; Start the Kafka server; Testing Kafka | |
| 520 | |a Chapter 3: Artificial Intelligence and Machine Learning; Artificial intelligence; Machine learning; Supervised learning; Unsupervised learning; Reinforced learning; Deep learning; Natural neuron; Artificial neuron; Weights; Activation function; Heaviside step function; Sigmoid function; Hyperbolic tangent function; Artificial neural network; Single-layer perceptron; Multi-layer perceptron; NLP; Cognitive computing; Machine learning pipelines in Apache Spark; Summary; Chapter 4: Supervised Learning Using Apache Spark; Linear regression; Case study - predicting bike sharing demand | ||
| 505 | 8 | |a Univariate linear regressionResiduals; Root mean square error; R-squared; Univariate linear regression in Apache Spark; Multivariate linear regression; Correlation; Multivariate linear regression in Apache Spark; Logistic regression; Threshold value; Confusion matrix; Receiver operator characteristic curve; Area under the ROC curve; Case study -- predicting breast cancer; Classification and Regression Trees; Case study -- predicting political affiliation; Random forests; K-Fold cross validation; Summary; Chapter 5: Unsupervised Learning Using Apache Spark; Clustering; Euclidean distance | |
| 500 | |a Hierarchical clustering | ||
| 520 | |a Machine Learning with Apache Spark provides a hands-on introduction to Big Data and Advanced Analytics. In a world driven by mass data creation and consumption, this book combines the latest scalable technologies with advanced analytical algorithms using real-world use-cases in order to derive actionable insights from Big Data in real-time. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 630 | 0 | 0 | |a Spark (Electronic resource : Apache Software Foundation) |
| 630 | 0 | 7 | |a Spark (Electronic resource : Apache Software Foundation) |2 fast |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Big data. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 7 | |a Data capture & analysis. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Data mining. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Database Management |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Quddus, Jillur. |t Machine Learning with Apache Spark Quick Start Guide : Uncover Patterns, Derive Actionable Insights, and Learn from Big Data Using MLlib. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781789346565 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5626693 |z Texto completo |
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| 938 | |a ProQuest Ebook Central |b EBLB |n EBL5626693 | ||
| 938 | |a YBP Library Services |b YANK |n 15914136 | ||
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