- 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
In this chapter, we had a closer look at modeling sequences of observations with hidden (or latent) states with the two commonly used algorithms:
- The generative hidden Markov model to maximize p(X,Y)
- The discriminative conditional random field to maximize log p(Y|X)
The HMM is a special form of Bayes network. It requires the observations to be independent. Although restrictive, the conditional independence prerequisites make the HMM fairly easy to understand and validate, which is not the case for a CRF.
You learned how to implement three dynamic programming techniques: Viterbi, Baum-Welch, and alpha/beta algorithms in Scala. These algorithms are used to solve diverse type of optimization problems. They should be an essential component of your algorithmic tool box.
The conditional random field relies on the logistic regression to estimate the optimal weights of the model. Such a technique is also used in the multiple layer perceptron, which was introduced in Chapter 9, Artificial Neural Network. The next chapter introduces two important alternatives to the logistic regression for discriminating between observations: the Kernel function for nonlinear models and the maximization of the margin between classes of observations.
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