Practical machine learning tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques
Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2016.
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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/alma991009630214206719 |
Tabla de Contenidos:
- Cover; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Preface; Chapter 1: Introduction to Machine learning; Machine learning; Definition; Core Concepts and Terminology; What is learning?; Data; Labeled and unlabeled data; Tasks; Algorithms; Models; Data and inconsistencies in Machine learning; Under-fitting; Over-fitting; Data instability; Unpredictable data formats; Practical Machine learning examples; Types of learning problems; Classification; Clustering; Forecasting, prediction or regression; Simulation; Optimization
- Supervised learningUnsupervised learning; Semi-supervised learning; Reinforcement learning; Deep learning; Performance measures; Is the solution good?; Mean squared error (MSE); Mean absolute error (MAE); Normalized MSE and MAE (NMSE and NMAE); Solving the errors: bias and variance; Some complementing fields of Machine learning; Data mining; Artificial intelligence (AI); Statistical learning; Data science; Machine learning process lifecycle and solution architecture; Machine learning algorithms; Decision tree based algorithms; Bayesian method based algorithms; Kernel method based algorithms
- Clustering methodsArtificial neural networks (ANN); Dimensionality reduction; Ensemble methods; Instance based learning algorithms; Regression analysis based algorithms; Association rule based learning algorithms; Machine learning tools and frameworks; Summary; Chapter 2: Machine learning and Large-scale datasets; Big data and the context of large-scale Machine learning; Functional versus Structural - A methodological mismatch; Commoditizing information; Theoretical limitations of RDBMS; Scaling-up versus Scaling-out storage; Distributed and parallel computing strategies
- Machine learning: Scalability and PerformanceToo many data points or instances; Too many attributes or features; Shrinking response time windows - need for real-time responses; Highly complex algorithm; Feed forward, iterative prediction cycles; Model selection process; Potential issues in large-scale Machine learning; Algorithms and Concurrency; Developing concurrent algorithms; Technology and implementation options for scaling-up Machine learning; MapReduce programming paradigm; High Performance Computing (HPC) with Message Passing Interface (MPI)
- Language Integrated Queries (LINQ) frameworkManipulating datasets with LINQ; Graphics Processing Unit (GPU); Field Programmable Gate Array (FPGA); Multicore or multiprocessor systems; Summary; Chapter 3: An Introduction to Hadoop's Architecture and Ecosystem; Introduction to Apache Hadoop; Evolution of Hadoop (the platform of choice); Hadoop and its core elements; Machine learning solution architecture for big data (employing Hadoop); The Data Source layer; The Ingestion layer; The Hadoop Storage layer; The Hadoop (Physical) Infrastructure layer - supporting appliance
- Hadoop platform / Processing layer
