By convention, the rare class is usually positive, so this means the True Positive (TP) rate is 0.78, and the False Negative rate (1 – True Positive rate) is 0.22. The Non-Large Loss recognition rate is 0.79, so the True Negative rate is 0.79 and the False Positive (FP) rate is 0.21. They don't report a False Positive rate (or True Negative rate, from which we could have calculated it). This result means that, using their Neural network, they must process 28 uninteresting Non-Large Loss customers (false alarms) for each Large-Loss customer they want.
With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.
Kirk will present a variety of atypical use cases and applications (in science and in business) of some typical textbook machine learning algorithms, including regression, clustering analysis, association mining, time series analysis, and network analysis. Who it's for: Anyone interested in machine learning, analytics and data science; Chief Digital Officers, Intrapreneurs, Chief Innovation Officers, Data Scientists, Developers. The applications that I discuss will cover different industries (healthcare, science, marketing, business, cyber), and therefore the applications are relevant to any industry, since the applications cover the basic types of discovery from data science: Class Discovery, Trend/Correlation Discovery, Novelty (Anomaly) Discovery, and Link (Association) Discovery --- those are applicable everywhere! Prior to that, he spent nearly 20 years supporting NASA projects, including NASA's Hubble Space Telescope as Data Archive Project Scientist, NASA's Astronomy Data Center, and NASA's Space Science Data Operations Office.
Najeeb Khan 51 views The Internet of Autonomous Vehicles - Keynote highlights by João Barros (Veniam CEO) at MWC17 - Duration: 3:52. João Barros 1,228 views How augmented intelligence is affecting data science and nuclear research - Duration: 15:01. SiliconANGLE 449 views Artificial Intelligence Takes the Wheel - Frost & Sullivan - CES 2016 - Duration: 7:01. The Internet of Autonomous Vehicles - Keynote highlights by João Barros (Veniam CEO) at MWC17 - Duration: 3:52.
Endowing the modern workforce with AI, machine learning, payment intelligence and advanced analytics fintech will thrive, amplify and fly. The most striking AI solutions to FinTech, banks, insurance companies (now called InsureTech) and any other financial services company will probably be those that have the robust & smart financial systems with data security, machine learning (machine conciseness is very far for now) and strong analytics features in place. AI technology such as specialized hardware, AI based operating systems, strong and large data analytics tools for big data, machine learning algorithms for machine intelligence, payment intelligence, data intelligence and info-security intelligence are being used in fintech to augment tasks that people already perform. With AI power to enable security features of mobile payments mean the technology could gain traction in other areas of B2B payments and escalate blockchain to generalize, any previous application of AI, but now the AI "owns itself".
Manual data science for industrial processes can be extremely counter-productive, especially when businesses embracing the IIoT are greatly emphasizing superior dexterity in operations. Today, data science and machine learning professionals are faced with a daunting challenge. That's where unsupervised learning combined with cognitive predictive maintenance comes into play. This is precisely how our Cognitive Predictive Maintenance (CPdM) platform works to help save thousands of valuable hours, automating tasks done by data scientists to make them vastly more efficient.
Artificial intelligence aims to revolutionize the hiring process by integrating technology with recruitment. Recruitment -- as a process -- is data driven, and by using this data in an effective way, Human Resources (HR) is able to gauge how successful an employee will be in an organisation. Artificial intelligence also incorporates employee experiences at previous companies as well as extra-curricular skills and other qualities. Since a large number of resumes are received by Human Resources (HR), there is a huge build-up of valuable data, which can be tabulated and analysed accurately, with the support of technology.
Also, these data science tutorials give you idea about data science, python, data scientist, big data, analytics, machine learning, deep learning and Artificial Intelligence (AI) are the most booming topics now. Description: Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Learn data visualization through Tableau 10 and create opportunities for you or key decision makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks. Learn data visualization through Microsoft Power BI and create opportunities for you or key decision makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks.
Franz Inc., in partnership with Montefiore Health System, is bringing the data lake to health IT using Franz's semantic graph database technology. Until its venture into the healthcare and pharmaceutical industries over the past few years, the 31-year-old Oakland, Calif., company had done business mainly in the worlds of national defense and intelligence, into which it sold its artificial intelligence-based triple store database that uses semantic, instead of relational, database technology. Using RDF technology, triple stores are a way to manage, manipulate and query many triples. Unlike most relational databases' linear representation and analysis of data, Franz's semantic graph database technology employs visual and spatial charting with which users can graphically see data elements and their relationships.
In effect, Live Data Map acts as a knowledge graph and metadata repository, and can help automation of data discovery and preparation tasks. Informatica company leaders pointed to Live Data Map as one among several signs of the company's commitment to innovation. They indicated the company has worked to improve performance of the open source Titan graph database on which Live Data Map, originally discussed at last year's edition of the conference, was built. He suggested Live Data Map and other technology activity at Informatica World 2016 show a company still very much in the game.