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Santa Clara Adult Education

Build Machine Learning Applications with Linux, Python, and Spark

 

 

Machine Learning Library: MLlib

MLlib is Spark's machine learning (ML) library. Its goal is to make practical machine learning scalable and easy.

  • Pipelines
  • Extracting, transforming and selecting features
  • Classification and Regression
  • Clustering
  • Collaborative filtering
  • Model selection and tunin

One way to create an instance of an algorithm is to create a Transformer. For example I can use a type of Transformer called a Model to read a DataFrame of features and output a DataFrame with a column of predictions.

Scala is a general-purpose programming language. Scala has full support for functional programming and a strong static type system. Designed to be concise,[10] many of Scala's design decisions were inspired by criticism of Java's shortcomings.

Example: linr11.scala vs. logr12.scla

The script should download prices and predict daily direction of GSPC. It should generate a label which I assume to be dependent on price calculations. A label should classify an observation as down or up. Down is 0.0, up is 1.0. It should generate independent features from slopes of moving averages of prices. It should create a Linear/Logistic Regression model from many years of features.

lir11.scala & logr12.scala

To be continue...

TensorFlow is an open source software library for machine learning in various kinds of perceptual and language understanding tasks. It is currently used for both research and production by 50 different teams in dozens of commercial Google products, such as speech recognition, Gmail, Google Photos, and search,many of which had previously used its predecessor DistBelief. TensorFlow was originally developed by the Google Brain team for Google's research and production purposes and later released under the Apache 2.0 open source license on November 9, 2015.

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

Neural Network

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions. Other advantages include:

1. Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.

2. Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.

3. Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.

4. Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

Node.js is an open-source, cross-platform JavaScript runtime environment for developing a diverse variety of tools and applications. Although Node.js is not a JavaScript framework, many of its basic modules are written in JavaScript, and developers can write new modules in JavaScript. The runtime environment interprets JavaScript using Google's V8 JavaScript engine. Node.js has an event-driven architecture capable of asynchronous I/O. These design choices aim to optimize throughput and scalability in Web applications with many input/output operations, as well as for real-time Web application.

Node.js® is a JavaScript runtime built on Chrome's V8 JavaScript engine. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient. Node.js' package ecosystem, npm, is the largest ecosystem of open source libraries in the world.

Node.js allows the creation of Web servers and networking tools using JavaScript and a collection of "modules" that handle various core functionality. Modules are provided for file system I/O, networking (DNS, HTTP, TCP, TLS/SSL, or UDP), binary data (buffers), cryptography functions, data streams and other core functions. Node.js's modules use an API designed to reduce the complexity of writing server applications.

Node.js is primarily used to build network programs such as Web servers, making it similar to PHP. The biggest difference between Node.js and PHP is that most functions in PHP block until completion (commands execute only after previous commands have completed), while functions in Node.js are designed to be non-blocking (commands execute in parallel, and use callbacks to signal completion or failure).

Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models.

Seaborn is a Python visualization library based on matplotlib that provides a high-level interface for drawing attractive statistical graphics. Seaborn is tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. Seaborn is a library for making attractive and informative statistical graphics in Python.

R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years.

Example: Use R to create a simple heatmap (source: NBA players 2008)

Code: hw10.r