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Scala for Machine Learning - Second Edition.
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
|
| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Nicolas, Patrick R. | |
| 245 | 1 | 0 | |a Scala for Machine Learning - Second Edition. |
| 250 | |a 2nd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing, |c 2017. | ||
| 300 | |a 1 online resource (740 pages) | ||
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| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a Step 4 -- Classification. | |
| 520 | |a 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 programming to the next level by creating and customizing AI applications Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial style Who This Book Is For If you're a data scientist or a data analyst with a fundamental knowledge of Scala who wants to learn and implement various Machine learning techniques, this book is for you. 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! What You Will Learn Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Write your own classification, clustering, or evolutionary algorithm Perform relative performance tuning and evaluation of Spark Master probabilistic models for sequential data Experiment with advanced techniques such as regularization and kernelization Dive into neural networks and some deep learning architecture Apply some basic multiarm-bandit algorithms Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to a technical analysis of financial markets In Detail The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naïve Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, s ... | ||
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| 650 | 0 | |a Scala (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Regression analysis |x Data processing. | |
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| 650 | 6 | |a Apprentissage automatique. | |
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| 776 | 0 | 8 | |i Print version: |a Nicolas, Patrick R. |t Scala for Machine Learning - Second Edition. |d Birmingham : Packt Publishing, ©2017 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5061334 |z Texto completo |
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