- 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
Unknown unknowns have been largely made famous due to a phrase from a response the United States Secretary of Defense, Donald Rumsfeld, gave to a question at a United States Department of Defense (DoD) news briefing on February 12, 2002 about the lack of evidence linking the government of Iraq with the supply of weapons of mass destruction to terrorist groups, and books by Nassim Taleb (The Black Swan: The Impact of the Highly Improbable by Nassim Taleb, Random House, 2007).
Note
Turkey paradox
Arguably, the unknown unknown is better explained by the turkey paradox. Suppose you have a family of turkeys playing in the backyard and enjoying protection and free food. Across the fence, there is another family of turkeys. This all works day after day, and month after month, until Thanksgiving comes—Thanksgiving Day is a national holiday celebrated in Canada and the United States, where it's customary to roast the turkeys in an oven. The turkeys are very likely to be harvested and consumed at this point, although from the turkey's point of view, there is no discernable signal that anything will happen on the second Monday of October in Canada and the fourth Thursday of November in the United States. No amount of modeling on the within-the-year data can fix this prediction problem from the turkey's point of view besides the additional year-over-year information.
The unknown unknown is something that is not in the model and cannot be anticipated to be in the model. In reality, the only unknown unknowns that are of interest are the ones that affect the model so significantly that the results that were previously virtually impossible, or possible with infinitesimal probability, now become the reality. Given that most of the practical distributions are from exponential family with really thin tails, the deviation from normal does not have to be more than a few sigmas to have devastating results on the standard model assumptions. While one has still to come up with an actionable strategy of how to include the unknown factors in the model—a few ways have been proposed, including fractals, but few if any are actionable—the practitioners have to be aware of the risks, and here the definition of the risk is exactly the possibility of delivering the models useless. Of course, the difference between the known unknown and unknown unknown is exactly that we understand the risks and what needs to be explored.
As we looked at the basic scope of problems that the decision-making systems are facing, let's look at the data pipelines, the software systems that provide information for making the decisions, and more practical aspects of designing the data pipeline for a data-driven system.
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