R can be used in production systems if it is productized, ruggedized, and assimilated into tools that developers and operations teams are familiar with. This involves:
1. Creating compelling data products and services with R that solve business problems.
2. Making R robust and production-ready through containerization, continuous integration testing, and separating development and production environments.
3. Integrating R into existing developer workflows by implementing HTTP APIs, Docker containers, and command line tools so it is easy for developers to use.
Health Check: Maintaining Enterprise BIEric Kavanagh
The document summarizes key points from a presentation on business intelligence (BI) monitoring. It discusses the importance of monitoring BI platforms to ensure performance and availability for users. It also introduces two speakers who will discuss insights driving modern businesses, challenges with performance, and how monitoring can help address issues and keep BI platforms running smoothly. The document provides an overview of topics that will be covered in the presentation.
Machine Learning and Azure ML Studio
This document outlines a presentation about machine learning and Azure ML Studio. It introduces machine learning concepts like AI and learning from examples. It then discusses how Azure ML Studio allows users to create models by getting and preprocessing data, defining features, training models using algorithms, and scoring/testing models. The document provides an example case scenario of using the platform to predict automobile prices by creating a model with automobile data, preprocessing it, training a random forest model, and scoring new automobile data.
High Performance Machine Learning in R with H2OSri Ambati
This document summarizes a presentation by Erin LeDell from H2O.ai about machine learning using the H2O software. H2O is an open-source machine learning platform that provides APIs for R, Python, Scala and other languages. It allows distributed machine learning on large datasets across clusters. The presentation covers H2O's architecture, algorithms like random forests and deep learning, and how to use H2O within R including loading data, training models, and running grid searches. It also discusses H2O on Spark via Sparkling Water and real-world use cases with customers.
Rapid Response: Debugging and Profiling to the RescueEric Kavanagh
The document provides an overview of debugging techniques for databases. It discusses the history of programming languages and when interactive debuggers first appeared. It describes common debugger functionality like breakpoints and stepping. It also covers profiling tools and examples of using debuggers and profilers to optimize database code. The presentation concludes with a demo of typical database debugging and profiling tools.
R can be used in production systems if it is productized, ruggedized, and assimilated into tools that developers and operations teams are familiar with. This involves:
1. Creating compelling data products and services with R that solve business problems.
2. Making R robust and production-ready through containerization, continuous integration testing, and separating development and production environments.
3. Integrating R into existing developer workflows by implementing HTTP APIs, Docker containers, and command line tools so it is easy for developers to use.
Health Check: Maintaining Enterprise BIEric Kavanagh
The document summarizes key points from a presentation on business intelligence (BI) monitoring. It discusses the importance of monitoring BI platforms to ensure performance and availability for users. It also introduces two speakers who will discuss insights driving modern businesses, challenges with performance, and how monitoring can help address issues and keep BI platforms running smoothly. The document provides an overview of topics that will be covered in the presentation.
Machine Learning and Azure ML Studio
This document outlines a presentation about machine learning and Azure ML Studio. It introduces machine learning concepts like AI and learning from examples. It then discusses how Azure ML Studio allows users to create models by getting and preprocessing data, defining features, training models using algorithms, and scoring/testing models. The document provides an example case scenario of using the platform to predict automobile prices by creating a model with automobile data, preprocessing it, training a random forest model, and scoring new automobile data.
High Performance Machine Learning in R with H2OSri Ambati
This document summarizes a presentation by Erin LeDell from H2O.ai about machine learning using the H2O software. H2O is an open-source machine learning platform that provides APIs for R, Python, Scala and other languages. It allows distributed machine learning on large datasets across clusters. The presentation covers H2O's architecture, algorithms like random forests and deep learning, and how to use H2O within R including loading data, training models, and running grid searches. It also discusses H2O on Spark via Sparkling Water and real-world use cases with customers.
Rapid Response: Debugging and Profiling to the RescueEric Kavanagh
The document provides an overview of debugging techniques for databases. It discusses the history of programming languages and when interactive debuggers first appeared. It describes common debugger functionality like breakpoints and stepping. It also covers profiling tools and examples of using debuggers and profilers to optimize database code. The presentation concludes with a demo of typical database debugging and profiling tools.
how to learn quantmod and quantstrat by yourselfChia-Chi Chang
This document provides instructions and resources for learning the R packages quantmod and quantstrat on your own. It lists the quantmod and quantstrat courses on DataCamp and links to the quantstrat documentation on GitHub to learn how to manipulate time series data and conduct financial trading simulations in R.
This document discusses communicating effectively with data by taking both a problem-driven and data-driven approach. It emphasizes understanding the problem behind the data as well as the information behind the problem to generate business insights. Both the problem and data should inform each other.
- The document provides an agenda for a presentation on mining trading strategies with R using quantstrat and R packages.
- It includes quick surveys of the audience, an overview of the architecture of a trading system, hands-on sessions on quantmod, PerformanceAnalytics, blotter and quantstrat, and discussions of basic concepts in quantitative trading and machine learning applications.
- The presenter is George Chang from Taiwan and organizes the Taiwan R User Group and MLDM Monday for applying machine learning in the real world through hands-on practice.
PyData SF 2016 --- Moving forward through the darknessChia-Chi Chang
This document discusses various types of "blindness" that can occur when applying machine learning modeling procedures and techniques. It notes that modeling procedures often focus on decomposing problems and data in a way that can lose important connections or information. Specific issues highlighted include the gap between problems and available data, information loss when converting data to vectors, disconnects between mathematical concepts and real-world applications, limitations of individual ML techniques, and challenges with new data and labels. The document advocates thinking more from both data-driven and problem-driven perspectives, and considering alternative techniques that can bridge gaps, such as metric learning and one-versus-all classifiers.
This document provides an overview of the book "Machine Learning for Hackers" and the MLDM Monday meetup. It summarizes the key points of each chapter, which cover basic R, supervised learning techniques like classification and regression, unsupervised learning techniques like PCA and clustering, and a concluding chapter on model comparison. Sample R codes from the book are available online. The meetup will introduce machine learning concepts and use two example datasets to practice basic data analysis and cleaning in R.
Learning notes of r for python programmer (Temp1)Chia-Chi Chang
R has several basic data types including integers, numerics, characters, complexes, and logicals. Objects in R include vectors, matrices, lists, data frames, factors, and environments. Functions like length(), mode(), class(), and str() can provide properties of R objects. R supports control structures like if/else, for loops, while loops, and repeat loops. R also has rich graphics capabilities for creating plots, histograms and other visualizations using both base and lattice graphics. Common packages used with R include those for statistics, machine learning, and working with time series and financial data.
how to learn quantmod and quantstrat by yourselfChia-Chi Chang
This document provides instructions and resources for learning the R packages quantmod and quantstrat on your own. It lists the quantmod and quantstrat courses on DataCamp and links to the quantstrat documentation on GitHub to learn how to manipulate time series data and conduct financial trading simulations in R.
This document discusses communicating effectively with data by taking both a problem-driven and data-driven approach. It emphasizes understanding the problem behind the data as well as the information behind the problem to generate business insights. Both the problem and data should inform each other.
- The document provides an agenda for a presentation on mining trading strategies with R using quantstrat and R packages.
- It includes quick surveys of the audience, an overview of the architecture of a trading system, hands-on sessions on quantmod, PerformanceAnalytics, blotter and quantstrat, and discussions of basic concepts in quantitative trading and machine learning applications.
- The presenter is George Chang from Taiwan and organizes the Taiwan R User Group and MLDM Monday for applying machine learning in the real world through hands-on practice.
PyData SF 2016 --- Moving forward through the darknessChia-Chi Chang
This document discusses various types of "blindness" that can occur when applying machine learning modeling procedures and techniques. It notes that modeling procedures often focus on decomposing problems and data in a way that can lose important connections or information. Specific issues highlighted include the gap between problems and available data, information loss when converting data to vectors, disconnects between mathematical concepts and real-world applications, limitations of individual ML techniques, and challenges with new data and labels. The document advocates thinking more from both data-driven and problem-driven perspectives, and considering alternative techniques that can bridge gaps, such as metric learning and one-versus-all classifiers.
This document provides an overview of the book "Machine Learning for Hackers" and the MLDM Monday meetup. It summarizes the key points of each chapter, which cover basic R, supervised learning techniques like classification and regression, unsupervised learning techniques like PCA and clustering, and a concluding chapter on model comparison. Sample R codes from the book are available online. The meetup will introduce machine learning concepts and use two example datasets to practice basic data analysis and cleaning in R.
Learning notes of r for python programmer (Temp1)Chia-Chi Chang
R has several basic data types including integers, numerics, characters, complexes, and logicals. Objects in R include vectors, matrices, lists, data frames, factors, and environments. Functions like length(), mode(), class(), and str() can provide properties of R objects. R supports control structures like if/else, for loops, while loops, and repeat loops. R also has rich graphics capabilities for creating plots, histograms and other visualizations using both base and lattice graphics. Common packages used with R include those for statistics, machine learning, and working with time series and financial data.