Scala for Machine Learning - Second Edition.

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Leverage Scala and Machine Learning to study and construct systems that can learn from data About This Book Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala Take your expertise in Scala p...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Nicolas, Patrick R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2017.
Edición:2nd ed.
Temas:
Scala (Computer program language)
Machine learning.
Regression analysis > Data processing.
Scala (Langage de programmation)
Apprentissage automatique.
Analyse de régression > Informatique.
COMPUTERS > Programming Languages > General.
Epitonium
Machine learning
Regression analysis > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Getting Started ; Mathematical notations for the curious; Why machine learning?; Classification; Prediction; Optimization; Regression; Why Scala?; Scala as a functional language; Abstraction; Higher kinded types; Functors; Monads; Scala as an object oriented language; Scala as a scalable language; Model categorization; Taxonomy of machine learning algorithms; Unsupervised learning; Clustering; Dimension reduction; Supervised learning; Generative models.
  • Discriminative modelsSemi-supervised learning; Reinforcement learning; Leveraging Java libraries; Tools and frameworks; Java; Scala; Eclipse Scala IDE; IntelliJ IDEA Scala plugin; Simple build tool; Apache Commons Math; Description; Licensing; Installation; JFreeChart; Description; Licensing; Installation; Other libraries and frameworks; Source code; Convention; Context bounds; Presentation; Primitives and implicits; Immutability; Let's kick the tires; Writing a simple workflow; Step 1
  • scoping the problem; Step 2
  • loading data; Step 3
  • preprocessing data; Step 4
  • discovering patterns.
  • Step 5
  • implementing the classifierStep 6
  • evaluating the model; Summary; Chapter 2: Data Pipelines ; Modeling; What is a model?; Model versus design; Selecting features; Extracting features; Defining a methodology; Monadic data transformation; Error handling; Monads to the rescue; mplicit models; Explicit models; Workflow computational model; Supporting mathematical abstractions; Step 1
  • variable declaration; Step 2
  • model definition; Step 3
  • instantiation; Composing mixins to build workflow; Understanding the problem; Defining modules; Instantiating the workflow; Modularizing.
  • Profiling dataImmutable statistics; Z-score and Gauss; Assessing a model; Validation; Key quality metrics; F-score for binomial classification; F-score for multinomial classification; Area under the curves; Area under PRC; Area under ROC; Cross-validation; One-fold cross-validation; K-fold cross-validation; Bias-variance decomposition; Overfitting; Summary; Chapter 3: Data Preprocessing ; Time series in Scala; Context bounds; Types and operations; Transpose operator; Differential operator; Lazy views; Moving averages; Simple moving average; Weighted moving average; Exponential moving average.
  • Fourier analysisDiscrete Fourier transform (DFT); DFT-based filtering; Detection of market cycles; The discrete Kalman filter; The state space estimation; The transition equation; The measurement equation; The recursive algorithm; Prediction; Correction; Kalman smoothing; Fixed lag smoothing; Experimentation; Benefits and drawbacks; Alternative preprocessing techniques; Summary; Chapter 4: Unsupervised Learning ; K-mean clustering; K-means; Measuring similarity; Defining the algorithm; Step 1
  • Clusters configuration; Step 2
  • Clusters assignment; Step 3
  • Reconstruction error minimization.
  • Step 4
  • Classification.

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