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Scala for machine learning : leverage Scala and machine learning to construct and study systems that can learn from data /
Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2014.
|
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 003 | OCoLC | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Nicolas, Patrick R., |e author. | |
| 245 | 1 | 0 | |a Scala for machine learning : |b leverage Scala and machine learning to construct and study systems that can learn from data / |c Patrick R. Nicolas. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2014. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from cover (Safari, viewed January 22, 2015). | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started; Mathematical notation for the curious; Why machine learning?; Classification; Prediction; Optimization; Regression; Why Scala?; Abstraction; Scalability; Configurability; Maintainability; Computation on demand; Model categorization; Taxonomy of machine learning algorithms; Unsupervised learning; Clustering; Dimension reduction; Supervised learning; Generative models; Discriminative models; Reinforcement learning; Tools and frameworks; Java; Scala | |
| 505 | 8 | |a Apache Commons MathDescription; Licensing; Installation; JFreeChart; Description; Licensing; Installation; Other libraries and frameworks; Source code; Context versus view bounds; Presentation; Primitives and implicits; Primitive types; Type conversions; Operators; Immutability; Performance of Scala iterators; Let's kick the tires; Overview of computational workflows; Writing a simple workflow; Selecting a dataset; Loading the dataset; Preprocessing the dataset; Creating a model (learning); Classify the data; Summary; Chapter 2: Hello World!; Modeling; A model by any other name | |
| 505 | 8 | |a Model versus designSelecting a model's features; Extracting features; Designing a workflow; The computational framework; The pipe operator; Monadic data transformation; Dependency injection; Workflow modules; The workflow factory; Examples of workflow components; The preprocessing module; The clustering module; Assessing a model; Validation; Key metrics; Implementation; K-fold cross-validation; Bias-variance decomposition; Overfitting; Summary; Chapter 3: Data Preprocessing; Time series; Moving averages; The simple moving average; The weighted moving average; The exponential moving average | |
| 505 | 8 | |a Fourier analysisDiscrete Fourier transform (DFT); DFT-based filtering; Detection of market cycles; The Kalman filter; The state space estimation; The transition equation; The measurement equation; The recursive algorithm; Prediction; Correction; Kalman smoothing; Experimentation; Alternative preprocessing techniques; Summary; Chapter 4: Unsupervised Learning; Clustering; K-means clustering; Measuring similarity; Overview of the K-means algorithm; Step 1 -- cluster configuration; Step 2 -- cluster assignment; Step 3 -- iterative reconstruction; Curse of dimensionality; Experiment | |
| 505 | 8 | |a Tuning the number of clustersValidation; Expectation-maximization (EM) algorithm; Gaussian mixture model; EM overview; Implementation; Testing; Online EM; Dimension reduction; Principal components analysis (PCA); Algorithm; Implementation; Test case; Evaluation; Other dimension reduction techniques; Performance considerations; K-means; EM; PCA; Summary; Chapter 5: Naïve Bayes Classifiers; Probabilistic graphical models; Naïve Bayes classifiers; Introducing the multinomial Naïve Bayes; Formalism; The frequentist perspective; The predictive model; The zero-frequency problem; Implementation | |
| 520 | |a Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book! | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Scala (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Regression analysis |x Data processing. | |
| 650 | 6 | |a Scala (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Analyse de régression |x Informatique. | |
| 650 | 7 | |a COMPUTERS |x Programming Languages |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Regression analysis |x Data processing. |2 fast |0 (OCoLC)fst01093274 | |
| 650 | 7 | |a Scala (Computer program language) |2 fast |0 (OCoLC)fst01763491 | |
| 776 | 0 | 8 | |i Print version: |a Nicolas, Patrick R. |t Scala for machine learning : leverage scala and machine learning to construct and study systems that can learn from data. |d Birmingham, [England] : Packt Publishing, ©2014 |h xi, 491 pages |k Community experience distilled. |z 9781783558742 |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781783558742/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH28059734 | ||
| 938 | |a ebrary |b EBRY |n ebr11001679 | ||
| 938 | |a EBSCOhost |b EBSC |n 929412 | ||
| 994 | |a 92 |b IZTAP | ||