Scala Applied Machine Learning .pdf

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Java 9 Concurrency Cookbook Second Edition Java 9 Concurrency Cookbook Second Edition Java 9 Concurrency Cookbook Second Edition Table of contents Scala: Applied Machine Learning Scala: Applied Machine Learning Credits Preface What this learning path covers What you need for this learning path Module 1 stalling the JDK Installing and using SBt Module 2 Module 3 Who this learning path is for Reader feedback Customer support Downloading the example code Errata iracy Questions L Module 1 Scala and data science D ata science Programming in data science Why Scala? Static typing and type inference Scala encourages immutability Scala and functional programs pointer uncertainty Easier parallelism Interoperability with Java When not to use scala Summary References 2. Manipulating Data with breeze Code examples Installing breeze Getting help on breeze Basic Breeze data types Vectors Dense and sparse vectors and the vector trait Matrices Bui lding vectors and matrices Advanced indexing and slicing Mutating vectors and matrices Matrix multiplication, transposition. and the orientation of vectors Data preprocessing and feature engineering Breeze- function optimization Numerical derivatives Regularization An example logistic regression Towards re-usable code Alternatives to breeze Summary References 3. Plotting with breeze-viz Diving into Breeze Customizing plots Customizing the line type More advanced scatter plots Multi-plot example-scatterplot matrix plots Managing without documentation Breeze-viz reference Data visualization beyond breeze-viz Summary 4. Parallel collections and futures Parallel collections Limitations of parallel collections Error handling Setting the parallelism level An example-cross-validation with parallel collections Futures Future composition- using a future's resul Blocking until completion Controlling parallel execution with execution contexts Futures example stock price fetcher Summary References 5. Scala and Sol through JDBC Interacting with JDBC First steps with JDBC Connecting to a database server Creating tables Inserting data Reading data JDBC Summary Functional wrappers for JDBC Safer JDBC connections with the loan pattern Enriching JDBC statements with the"pimp my library"pattern Wrapping result sets in a stream Looser coupling with type classes Type classes Coding against type classes When to use type classes Benefits of type classes Creating a data access layer Summary References 6. Slick- A Functional Interface for SQL feC data Importing Slick Defining the schema Connecting to the database Creating tables Inserting data Querying data Inⅴ akers Operations on columns Aggregations with"Group by Accessing database metadata Slick versuS jdbc Summary References 7. Web aPis A whirlwind tour fison Querying web APIs JsoN in Scala- an exercise in pattern matching JSON4S types Extracting fields using XPath Extraction using case classes Concurrency and exception handling with futures Authentication-adding htTp headers Http-a whirlwind overview Adding headers to htTp reQuestS in Scala Summary References 8. Scala and MongoDB MongoDB Connecting to MongoDB with Casbah Connecting with authentication Inserting documents Extracting objects from the database Complex queries Casbah query dsl Custom type serialization Beyond casbah Summary References 9. Concurrency with Akka GitHub follower graph Actors as people Hello world with Akka Case classes as messages Actor construction anatomy of an actor Follower network crawler Fetcher actors Routing Message passing between actors Queue control and the pull pattern Accessing the sender of a message Stateful actors Follower network crawler Fault tolerance Custom supervisor strategies fe-cycle hooks What we have not talked about ummar references 10. Distributed Batch Processing with Spark Installing Spark acquiring the example data Resilient distributed datasets RDDs are immutable RDDs are lazy RDDs know their lineage RDDs are resilient RDDs are distributed Trans formations and actions on rdds Persisting RDDs Key-value Rdds Double rdds Building and running standalone programs Running Spark applications locally Reducing logging output and Spark configuration Running spark applications on ec2 Spam filtering Lifting the hood Data shuffling and partitions Summar Reference 11. Spark SQL and DataFrames Data frames -a whirlwind introduction Aggregation operations oining data Frames together Custom functions on data frames Data Frame immutability and persistence SOL Statements on Data Frames Complex data types-arrays, maps, and structs Structs Arraⅴs aps Interacting with data sources ISON files Parquet files Standalone programs Summary References 12. Distributed Machine Learning with MLlib Introducing MLlib- Spam classification Pipeline components Transformers Estimators Evaluation Regularization in logistic regression Cross-validation and model selection Beyond logistic regression Summary References 13. Web APis with Play Client-server applications Introduction to web frameworks Model-View-Controller architecture Single page applications Building an application he Play framework ynamic routng Actions Composing the response Understanding and parsing the request nteracting with JSON Querying external APIs and consuming JSON Calling external web services Parsing jso ON Asynchronous actions Creating APIs with Play: a summary Rest APls: best practice S ummary References 14. Visualization with d3 and the play framework Github user data o need a bac ken JavaScript dependencies through web-iars Towards a web application: hTML templates Modular JavaScript through requireS Bootstrapping the applications Client-Side program architecture Designing the model The event bus AJAX calls through JQuery Response views Drawing plots with NVD3 Summary References A Pattern matching and extractors Pattern matching in for comprehensions Pattern matching internals Extracting sequences ummary Reference Ⅱ. Module2 I. Getting Started Mathematical notation for the curious Why machine learning? Classification Prediction optimization Regression Why Scala? Abstraction Higher-kind projection Covariant functors for vectors Contravariant functors for co-vectors Monads Scalability Configurability Maintainability Computation on demand Model categorization Taxonomy of machine learning algorithms Unsupervised learning Clustering Dimension reduction Supervised learning Generative models Discriminative models Semi-supervised learning Reinforcement learning Don't reinvent the wheel Tools and frameworks ava Scala Apache Commons math Description licensing Installation

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