Apache spark 2.x machine learning cookbook over 100 recipes to simplify machine learning model implementations with Spark
Simplify machine learning model implementations with Spark About This Book Solve the day-to-day problems of data science with Spark This unique cookbook consists of exciting and intuitive numerical recipes Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Wh...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
2017.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630487606719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Practical Machine Learning with Spark Using Scala
- Introduction
- Apache Spark
- Machine learning
- Scala
- Software versions and libraries used in this book
- Downloading and installing the JDK
- Getting ready
- How to do it...
- Downloading and installing IntelliJ
- Getting ready
- How to do it...
- Downloading and installing Spark
- Getting ready
- How to do it...
- Configuring IntelliJ to work with Spark and run Spark ML sample codes
- Getting ready
- How to do it...
- There's more...
- See also
- Running a sample ML code from Spark
- Getting ready
- How to do it...
- Identifying data sources for practical machine learning
- Getting ready
- How to do it...
- See also
- Running your first program using Apache Spark 2.0 with the IntelliJ IDE
- How to do it...
- How it works...
- There's more...
- See also
- How to add graphics to your Spark program
- How to do it...
- How it works...
- There's more...
- See also
- Chapter 2: Just Enough Linear Algebra for Machine Learning with Spark
- Introduction
- Package imports and initial setup for vectors and matrices
- How to do it...
- There's more...
- See also
- Creating DenseVector and setup with Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Creating SparseVector and setup with Spark
- How to do it...
- How it works...
- There's more...
- See also
- Creating dense matrix and setup with Spark 2.0
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Using sparse local matrices with Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Performing vector arithmetic using Spark 2.0.
- How to do it...
- How it works...
- See also
- Performing matrix arithmetic using Spark 2.0
- How to do it...
- How it works...
- Exploring RowMatrix in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Exploring Distributed IndexedRowMatrix in Spark 2.0
- How to do it...
- How it works...
- See also&
- #160
- Exploring distributed CoordinateMatrix in Spark 2.0
- How to do it...
- How it works...
- See also&
- #160
- Exploring distributed BlockMatrix in Spark 2.0
- How to do it...
- How it works...
- See also&
- #160
- Chapter 3: Spark&
- #x27
- s Three Data Musketeers for Machine Learning - Perfect Together
- Introduction
- RDDs - what started it all...
- DataFrame - a natural evolution to unite API and SQL via a high-level API
- Dataset - a high-level unifying Data API
- Creating RDDs with Spark 2.0 using internal data sources
- How to do it...
- How it works...
- Creating RDDs with Spark 2.0 using external data sources
- How to do it...
- How it works...
- There's more...
- See also
- Transforming RDDs with Spark 2.0 using the filter() API
- How to do it...
- How it works...
- There's more...
- See also
- Transforming RDDs with the super useful flatMap() API
- How to do it...
- How it works...
- There's more...
- See also
- Transforming RDDs with set operation APIs
- How to do it...
- How it works...
- See also
- RDD transformation/aggregation with groupBy() and reduceByKey()
- How to do it...
- How it works...
- There's more...
- See also
- Transforming RDDs with the zip() API
- How to do it...
- How it works...
- See also
- Join transformation with paired key-value RDDs
- How to do it...
- How it works...
- There's more...
- Reduce and grouping transformation with paired key-value RDDs
- How to do it...
- How it works.
- See also
- Creating DataFrames from Scala data structures
- How to do it...
- How it works...
- There's more...
- See also
- Operating on DataFrames programmatically without SQL
- How to do it...
- How it works...
- There's more...
- See also
- Loading DataFrames and setup from an external source
- How to do it...
- How it works...
- There's more...
- See also
- Using DataFrames with standard SQL language - SparkSQL
- How to do it...
- How it works...
- There's more...
- See also
- Working with the Dataset API using a Scala Sequence
- How to do it...
- How it works...
- There's more...
- See also
- Creating and using Datasets from RDDs and back again
- How to do it...
- How it works...
- There's more...
- See also
- Working with JSON using the Dataset API and SQL together
- How to do it...
- How it works...
- There's more...
- See also
- Functional programming with the Dataset API using domain objects
- How to do it...
- How it works...
- There's more...
- See also
- Chapter 4: Common Recipes for Implementing a Robust Machine Learning System
- Introduction
- Spark's basic statistical API to help you build your own algorithms
- How to do it...
- How it works...
- There's more...
- See also
- ML pipelines for real-life machine learning applications
- How to do it...
- How it works...
- There's more...
- See also
- Normalizing data with Spark
- How to do it...
- How it works...
- There's more...
- See also
- Splitting data for training and testing
- How to do it...
- How it works...
- There's more...
- See also
- Common operations with the new Dataset API
- How to do it...
- How it works...
- There's more...
- See also
- Creating and using RDD versus DataFrame versus Dataset from a text file in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also.
- LabeledPoint data structure for Spark ML
- How to do it...
- How it works...
- There's more...
- See also
- Getting access to Spark cluster in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Getting access to Spark cluster pre-Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Getting access to SparkContext vis-a-vis SparkSession object in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- New model export and PMML markup in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Regression model evaluation using Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Binary classification model evaluation using Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Multiclass classification model evaluation using Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Multilabel classification model evaluation using Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Using the Scala Breeze library to do graphics in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Chapter 5: Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I
- Introduction
- Fitting a linear regression line to data the old fashioned way
- How to do it...
- How it works...
- There's more...
- See also
- Generalized linear regression in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Linear regression API with Lasso and L-BFGS in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Linear regression API with Lasso and 'auto' optimization selection in Spark 2.0
- How to do it...
- How it works...
- There's more.
- See also
- Linear regression API with ridge regression and 'auto' optimization selection in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Isotonic regression in Apache Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Multilayer perceptron classifier in Apache Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- One-vs-Rest classifier (One-vs-All) in Apache Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Survival regression - parametric AFT model in Apache Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Chapter 6: Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II
- Introduction
- Linear regression with SGD optimization in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Logistic regression with SGD optimization in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Ridge regression with SGD optimization in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Lasso regression with SGD optimization in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Logistic regression with L-BFGS optimization in Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Support Vector Machine (SVM) with Spark 2.0
- How to do it...
- How it works...
- There's more...
- See also
- Naive Bayes machine learning with Spark 2.0 MLlib
- How to do it...
- How it works...
- There's more...
- See also
- Exploring ML pipelines and DataFrames using logistic regression in Spark 2.0
- Getting ready
- How to do it...
- How it works...
- There's more...
- PipeLine
- Vectors
- See also.
- Chapter 7: Recommendation Engine that Scales with Spark.
