On a summer day in 1968, professor Julian Stanley met a brilliant but bored 12-year-old named Joseph Bates. The Baltimore student was so far ahead of his classmates in mathematics that his parents had arranged for him to take a computer-science course at Johns Hopkins University, where Stanley taught. Having leapfrogged ahead of the adults in the class, the child kept himself busy by teaching the FORTRAN programming language to graduate students. Unsure of what to do with Bates, his computer instructor introduced him to Stanley, a researcher well known for his work in psychometrics -- the study of cognitive performance. To discover more about the young prodigy's talent, Stanley gave Bates a battery of tests that included the SAT college-admissions exam, normally taken by university-bound 16- to 18-year-olds in the United States. Bates's score was well above the threshold for admission to Johns Hopkins, and prompted Stanley to search for a local high school that would let the child take advanced mathematics and science classes.

Abstract: Attendees will learn the foundations of text mining approaches in addition to learn basic text mining scripting functions used in R. The audience will learn what text mining is, then perform primary text mining such as keyword scanning, dendogram and word cloud creation. Later participants will be able to do more sophisticated analysis including polarity, topic modeling and named entity recognition. Bio: Ted Kwartler is the Director of Customer Success at DataRobot where he manages the end-to-end customer journey. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators.

An insistent pattern has quietly taken hold in my household. I will order some consumer product online. I will open the package, extract the thing from its protective wrappings, and retrieve the instruction manual. I will examine the product briefly, then begin to read the instruction manual. And then I will go to YouTube.

For decades, science fiction authors, futurists, and movie makers alike have been predicting the amazing (and sometimes catastrophic) changes that will arise with the advent of widespread artificial intelligence. So far, AI hasn't made any such crazy waves, and in many ways has quietly become ubiquitous in numerous aspects of our daily lives. From the intelligent sensors that help us take perfect pictures, to the automatic parking features in cars, to the sometimes frustrating personal assistants in smartphones, artificial intelligence of one kind of another is all around us, all the time. While we've yet to create self-aware robots like those that pepper popular movies like 2001: A Space Odyssey and Star Wars, we have made smart and often significant use of AI technology in a wide range of applications that, while not as mind-blowing as androids, still change our day-to-day lives. One place where artificial intelligence is poised to make big changes (and in some cases already is) is in education.

XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python. The idea of boosting came out of the idea of whether a weak learner can be modified to become better.

A universal condition of future U.S. armed interventions is the dizzying amount of data that American forces will have thrust upon them at, each level of war and in every dimension of combat. The single most important thing the U.S. military can do to adapt to the Information Age is to channel the impending torrent of information, from a multiplicity of data sources, to relevant decision makers in useful forms. The face of battle in this era will still be defined by blood and hardship, endured by small groups of people surviving their way to the next objective, but in a much more complex context. That complexity will be apparent, often paralyzing, when flooding through a cornucopia of sensor systems. Computers will help stem the tide, but distributed human innovation, in the space between information and knowledge, is the only force that can contextualize the flow.

For the last eight months, I have spent a lot of time trying to absorb as much as I can about machine learning. I am constantly amazed at the variety of people I meet on online MOOCs in this small but quickly growing community, from quantum researchers at Fermilab to Tesla-driving Silicon Valley CEOs. Lately, I have been putting a lot of my focus into the open source software TensorFlow, and this tutorial is the result of that. I feel like a lot of machine learning tutorials are geared toward Mac. One major advantage of using Linux is it's free and it supports using TensorFlow with your GPU.

Deep Learning is one of the key parts of data science. As data becomes increasingly important and accessible, today's biggest companies are rapidly investing in deep learning. In fact, it is considered to be so vital to future technologies that many are sharing their own results and discoveries with the public. Researchers have been playing with the idea of deep learning for decades, but it has only blossomed in recent years. With companies like Facebook and Google pouring funds and resources into research, consumers are finally seeing the results of deep learning for themselves.

This article is the fifth part of the tutorial on Clustering with DPMM. In the previous posts we covered in detail the theoretical background of the method and we described its mathematical representationsmu and ways to construct it. In this post we will try to link the theory with the practice by introducing two models DPMM: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model which is used to cluster documents. Update: The Datumbox Machine Learning Framework is now open-source and free to download. Check out the package com.datumbox.framework.machinelearning.clustering to see the implementation of Dirichlet Process Mixture Models in Java.

On a summer day in 1968, professor Julian Stanley met a brilliant but bored 12-year-old named Joseph Bates. The Baltimore student was so far ahead of his classmates in mathematics that his parents had arranged for him to take a computer-science course at Johns Hopkins University, where Stanley taught. Having leapfrogged ahead of the adults in the class, the child kept himself busy by teaching the FORTRAN programming language to graduate students. Unsure of what to do with Bates, his computer instructor introduced him to Stanley, a researcher well known for his work in psychometrics--the study of cognitive performance. To discover more about the young prodigy's talent, Stanley gave Bates a battery of tests that included the SAT college-admissions exam, normally taken by university-bound 16- to 18-year-olds in the United States.