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!
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, [England] :
Packt Publishing
2014.
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| Edición: | 1st edition |
| Colección: | Community experience distilled.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629199606719 |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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
- Software design
