Benefits and limitations
by Patrick R. Nicolas, Arun Manivannan, Pascal Bugnion
Scala: Guide for Data Science Professionals
- Scala: Guide for Data Science Professionals
- Table of Contents
- Scala: Guide for Data Science Professionals
- Scala: Guide for Data Science Professionals
- Credits
- Preface
- 1. 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 with Breeze
- Introduction
- Getting Breeze – the linear algebra library
- Working with vectors
- Getting ready
- How to do it...
- Creating vectors
- Constructing a vector from values
- Creating a vector out of a function
- Creating a vector of linearly spaced values
- Creating a vector with values in a specific range
- Creating an entire vector with a single value
- Slicing a sub-vector from a bigger vector
- Creating a Breeze Vector from a Scala Vector
- Vector arithmetic
- Scalar operations
- Calculating the dot product of two vectors
- Creating a new vector by adding two vectors together
- Appending vectors and converting a vector of one type to another
- Concatenating two vectors
- Standard deviation
- Find the largest value in a vector
- Finding the sum, square root and log of all the values in the vector
- Working with matrices
- Vectors and matrices with randomly distributed values
- How it works...
- Creating vectors with uniformly distributed random values
- Creating vectors with normally distributed random values
- Creating vectors with random values that have a Poisson distribution
- Creating a matrix with uniformly random values
- Creating a matrix with normally distributed random values
- Creating a matrix with random values that has a Poisson distribution
- How it works...
- Reading and writing CSV files
- 2. Getting Started with Apache Spark DataFrames
- Introduction
- Getting Apache Spark
- Creating a DataFrame from CSV
- Manipulating DataFrames
- Creating a DataFrame from Scala case classes
- 3. Loading and Preparing Data – DataFrame
- 4. Data Visualization
- 5. Learning from Data
- Introduction
- Supervised and unsupervised learning
- Gradient descent
- Predicting continuous values using linear regression
- Binary classification using LogisticRegression and SVM
- Binary classification using LogisticRegression with Pipeline API
- How to do it...
- Importing and splitting data as test and training sets
- Construct the participants of the Pipeline
- Preparing a pipeline and training a model
- Predicting against test data
- Evaluating a model without cross-validation
- Constructing parameters for cross-validation
- Constructing cross-validator and fit the best model
- Evaluating the model with cross-validation
- How to do it...
- Clustering using K-means
- Feature reduction using principal component analysis
- How to do it...
- Dimensionality reduction of data for supervised learning
- Mean-normalizing the training data
- Extracting the principal components
- Preparing the labeled data
- Preparing the test data
- Classify and evaluate the metrics
- Dimensionality reduction of data for unsupervised learning
- Mean-normalizing the training data
- Extracting the principal components
- Arriving at the number of components
- Evaluating the metrics
- How to do it...
- 6. Scaling Up
- 7. Going Further
- 1. Getting Started with Breeze
- III. Module 3
- 1. Getting Started
- 2. Hello World!
- 3. Data Preprocessing
- 4. Unsupervised Learning
- 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 (FFNN)
- The multilayer perceptron (MLP)
- The activation function
- The network architecture
- Software design
- Model definition
- Training cycle/epoch
- Training strategies and classification
- Evaluation
- Benefits and limitations
- Summary
- 10. Genetic Algorithms
- 11. Reinforcement Learning
- 12. Scalable Frameworks
- B. Basic Concepts
- C. Bibliography
- Index
The advantages and disadvantages of neural networks depend on which other machine learning methods they are compared to. However, neural-network-based classifiers, particularly the multilayer perceptron using error backpropagation, have some obvious advantages, such as:
- The mathematical foundation of a neural network does not require expertise in dynamic programming or linear algebra, beyond the basic gradient descent algorithm.
- A neural network can perform tasks that a linear algorithm cannot.
- MLP is usually reliable for highly dynamic and nonlinear processes. Contrary to the support vector machines, they do not require us to increase the problem dimension through kernelization.
- MLP does not make any assumption on linearity, variable independence, or normality.
- The execution of training of the MLP lends itself to concurrent processing quite well for online training. In most architecture, the algorithm can continue even if a node in the network fails.
However, as with any machine learning algorithm, neural networks have their detractors. Among the most documented limitations are as follows:
- MLP models are black boxes for which the association between features and classes may not be easily described.
- MLP requires a lengthy training process, especially using the batch strategy. For example, a two-layer network has a time complexity (number of multiplications) of O(n.m.p.N.e) for n input variables, m hidden neurons, p output values, N observations, and e epochs. It is not uncommon that a solution emerges after thousands of epochs. The online training strategy using momentum factor tends to converge faster and require a smaller number of epochs than the batch process.
- Tuning the configuration parameters, such as learning rate, selection of the activation method, application of softmax transformation, or momentum factor, can turn into a lengthy process.
- Estimating the minimum size of the training set to get accurate results is not obvious.
- A neural network cannot be incrementally retrained. Any new labeled data requires an entirely new training cycle.
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