Tag: Classification

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  Purpose Built Analytic Modules (PBAMs) such as those for Fraud Detection represent a fourth way to practice data science, a new model for the good use of Citizen Data Scientists, and a new market for AI-first companies. It appears that data science has exited its age of exploration and entered into its age of consolidation and refinement.  That doesn’t mean that we aren’t making improvements but increasingly these tend to be incremental and not the big exciting break throughs we made through about 2016.  More like a mission ... Read More

Feed: R-bloggers. Author: Dr. Shirin Glander. Today I am very happy to announce that during my stay in London for the m3 conference, I’ll also be giving a talk at the R-Ladies London Meetup on Tuesday, October 16th, about one of my favorite topics: Interpretable Deep Learning with R, Keras and LIME. You can register via Eventbrite: https://www.eventbrite.co.uk/e/interpretable-deep-learning-with-r-lime-and-keras-tickets-50118369392 ABOUT THE TALK Keras is a high-level open-source deep learning framework that by default works on top of TensorFlow. Keras is minimalistic, efficient and highly flexible because it works with a modular layer system to define, compile and fit neural networks. It ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Hemant Warudkar. The most obvious thing for any Health Care Organization (HCO) to do is to develop in-house infrastructure to manage its healthcare data. That’s simpler said than done, though. Setting up an Enterprise-grade data warehouse not only involves additional hires, there’s some amount of capital expenditure as well as operational costs involved. Here’s what’s needed – an investment in the right personnel and the right technology, data warehouse, analytics software, database experts, data architects, and analysts – all of which are required to handle the data & analyze it, & ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: hao-xin; As a kid, I dreamed about the flying cars and jetpacks that would hopefully define our future lives. Up until recently, the technology that could make them even remotely possible was both expensive and difficult to manufacture. Now, thanks to 3-D printing and machine learning, engineers can quickly and cost effectively design, test and commercialize new designs, making the era of autonomous vehicles — yes, even flying cars — closer to reality than ever.After years of combined academic and industry expertise in millimeter wave technology, I discovered a way ... Read More

Feed: AWS Government, Education, & Nonprofits Blog. Author: publicsector. A guest post by Brendan Walsh, Senior Vice President, Partner Relations, 1901 GroupAs federal fiscal year 2018 comes to an end and with budgets to spend and IT plans in place for 2019, the questions of “how and where do we start migrating mission-critical applications to the cloud?” still loom on many federal IT executives. Even with funding available and the need for IT modernization, these questions are tough because mission-critical applications often have substantial system entanglement challenges, inter-agency workflows, software-license restrictions, proprietary platform and vendor lock-in issues, and complicated budget ... Read More

Feed: R-bloggers. Author: Tony Lin. Congested streets and slow-crawling traffic are a fact of life in many metropolitan areas, such as New York City, Los Angeles, and Chicago. Bike sharing is an innovative solution for such problems, and it works by dispersing a large fleet of publicly-available bikes throughout crowded cities for personal transport. Implemented in 2013, Ford GoBike is the first bike-sharing system introduced in the US West Coast. Its 540 stations and 7,000 bikes sprawl across five cities in San Francisco Bay Area. A docked bike can be checked out at any station and must be returned to ... Read More

Feed: R-bloggers. Author: ntguardian. Introduction The Kolmogorov distribution (which I call ) is as follows: There is no known simpler form and we have to work with this sum as it is. This is an infinite sum. How can we compute the value of this infinite sum numerically? Naïvely we can do the following: summand [1] 0.7300003 In other words, sum up many of the terms and you should be close to the actual infinite sum. This is a crude approach. The answer is not wrong (numerically) but certainly we should understand why adding up that many terms works. Also, ... Read More

Feed: R-bloggers. Author: Chi Ting Low. Blue Bikes is a bicycle sharing system in the Boston, Massachusetts. The bikes sharing program started on 28 July 2011. This program aimed for individuals to use it for short-term basis for a price. The program allows individuals to borrow a bike from a dock station and retrun to another dock station after using it. According to 2017 report, there are more than 1600 bicycles in used. The aims of the current project are to gain insight of the user who used this program such as gender. It also aimed to investigate the duration ... Read More

Feed: Microsoft Azure Blog. Author: Diego Tamburini. In a previous blog article Extracting actionable insights from IoT data I discuss the value of collecting IoT data from machines, assets and products. The whole point of collecting IoT data is to extract actionable insights. Insights that will trigger some sort of action that will result in some business value such as optimized factory operations, improved product quality, better understanding of customer demand, new sources of revenue, and improved customer experience. In this blog, I discuss the extraction of value out of IoT data by focusing on the analytics part of the ... Read More

Feed: Planet big data. Author: Ricky Ho. In classical prediction use case, the predicted output is either a number (for regression) or category (for classification).  A set of training data (x, y) where x is the input and y is the labeled output is provided to train a parameterized predictive model.The model is characterized by a set of parameters w Given an input x, for the model predicts y_hat = f(x; w) for regression, or the model predicts the probability of each possible class for classification Define a Lost function L(y, y_hat) for regression, or L(y, P(y=a | x), P(y=b ... Read More