Don't reinvent the wheel!
by Alex Kozlov, Patrick R. Nicolas, Pascal Bugnion
Scala:Applied Machine Learning
- Scala:Applied Machine Learning
- Table of Contents
- Scala:Applied Machine Learning
- Scala:Applied Machine Learning
- Credits
- Preface
- I. Module 1
- 1. Scala and Data Science
- 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
- Building 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
- 4. Parallel Collections and Futures
- 5. Scala and SQL through JDBC
- Interacting with JDBC
- First steps with JDBC
- 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
- Creating a data access layer
- Summary
- References
- 6. Slick – A Functional Interface for SQL
- 7. Web APIs
- 8. Scala and MongoDB
- 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
- Life-cycle hooks
- What we have not talked about
- Summary
- References
- 10. Distributed Batch Processing with Spark
- 11. Spark SQL and DataFrames
- DataFrames – a whirlwind introduction
- Aggregation operations
- Joining DataFrames together
- Custom functions on DataFrames
- DataFrame immutability and persistence
- SQL statements on DataFrames
- Complex data types – arrays, maps, and structs
- Interacting with data sources
- Standalone programs
- Summary
- References
- 12. Distributed Machine Learning with MLlib
- 13. Web APIs with Play
- Client-server applications
- Introduction to web frameworks
- Model-View-Controller architecture
- Single page applications
- Building an application
- The Play framework
- Dynamic routing
- Actions
- Interacting with JSON
- Querying external APIs and consuming JSON
- Creating APIs with Play: a summary
- Rest APIs: best practice
- Summary
- References
- 14. Visualization with D3 and the Play Framework
- A. Pattern Matching and Extractors
- II. Module 2
- 1. Getting Started
- 2. Hello World!
- 3. Data Preprocessing
- 4. Unsupervised Learning
- Clustering
- K-means clustering
- The expectation-maximization algorithm
- Dimension reduction
- Performance considerations
- Summary
- Clustering
- 5. Naïve Bayes Classifiers
- 6. Regression and Regularization
- 7. Sequential Data Models
- 8. Kernel Models and Support Vector Machines
- 9. Artificial Neural Networks
- Feed-forward neural networks
- The multilayer perceptron
- Evaluation
- Convolution neural networks
- Benefits and limitations
- Summary
- 10. Genetic Algorithms
- 11. Reinforcement Learning
- 12. Scalable Frameworks
- A. Basic Concepts
- III. Module 3
- 1. Exploratory Data Analysis
- 2. Data Pipelines and Modeling
- 3. Working with Spark and MLlib
- 4. Supervised and Unsupervised Learning
- 5. Regression and Classification
- 6. Working with Unstructured Data
- 7. Working with Graph Algorithms
- 8. Integrating Scala with R and Python
- 9. NLP in Scala
- 10. Advanced Model Monitoring
- A. Bibliography
- Index
There are numerous robust, accurate, and efficient Java libraries for mathematics, linear algebra, or optimization that have been widely used for many years:
- JBlas/Linpack (https://github.com/mikiobraun/jblas)
- Parallel Colt (https://github.com/rwl/ParallelColt)
- Apache Commons Math (http://commons.apache.org/proper/commons-math)
There is absolutely no need to rewrite, debug, and test these components in Scala. Developers should consider creating a wrapper or interface to his/her favorite and reliable Java library. The book leverages the Apache Commons Math library for some specific linear algebra algorithms.
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