FOR NOTES WHTSAP-9355192609 FB PAGE:-https://www.facebook.com/MYKSACADEMY/ EMAIL:[email protected] INSTA ID:-iamksacademy
Views: 341 KS ACADEMY
Dubbed the leading successor to Hadoop MapReduce, Apache Spark is a cluster compute system that makes data analytics fast -- both fast to run and fast to write. Programs written in Spark can often outperform those in MapReduce by 100X, while being 10X shorter and more understandable. In addition, Spark also provides efficient support for streaming, query execution, machine learning, and graph computation through rich high level libraries. Last but not least, the project features one of the most active open source community in Big Data: 150+ developers from 30+ organizations have contributed code to the project. In this talk, we will introduce the project, survey the high level libraries including streaming, SQL, and machine learning, and expand into how Spark can help you make better decisions easier and faster. Speakers Reynold Xin is a committer on Apache Spark and a co-founder of Databricks. He is instrumental in the development of many high level frameworks on Spark, including SQL and graph computation. Prior to Databricks, he was pursuing a PhD in the UC Berkeley AMPLab. Patrick Wendell is a committer on Apache Spark and a co-founder of Databricks. Before Databricks, he was pursuing a PhD in the UC Berkeley AMPLab, where he worked on scalable low latency scheduling for data processing frameworks. In the past, he has contributed to several Hadoop projects, including Apache Flume and Apache Avro.
Views: 739 HiveMumble
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 19014 5 Minutes Engineering
Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 65 myproject bazaar
Views: 96 Yan ZHANG
RRW - A Robust and Reversible Watermarking Technique for Relational Data We develop projects in following technology JAVA/J2EE NS2 DOTNET ANDROID HADOOP / BIG DATA MATLAB ieee projects are available ready to purchase We are providing projects at low cost with good support. Please call us for further enquiry Data Alcott Systems [email protected] Ph: 044- 43314035 Mobile: (0) 9600095046 / 47 https://www.finalsemprojects.com https://www.facebook.com/ieeeprojects
Views: 545 finalsemprojects
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 49411 edureka!
Do data mining One way that some corporations keep ahead of their competition is to do data mining. Businesses derive useful information from huge databases through statistical analysis. Applications of this mathematical algorithm based analysis tool are in the areas of product analysis, consumer research marketing, e-Commerce, stock investment trend and many more. Relational database mining, web mining, text mining, audio and video mining, and social networks mining are some types of data mining. You can relate data mining to geology in the sense that in geology you search for specific minerals (for example gold or lead), while a statistical data miner uses various tools to find useful information from a wide database. It is a way of extracting data from very large and sometimes complex databases to find patterns or trends that a company can use to further their business. Data mining is a labor intensive job wherein a lot of data has to be collected and analyzed. Outsourcing data mining jobs may be more beneficial to companies who do not have the time or manpower to invest in this endeavor. The outsourcing company will take care of collecting the needed data and organizing the data in a well mapped database so that they can easily filter or extract the required information for analysis. But if you have the resources, you can also use a variety of data mining programs out there. Some data mining software are SAS Enterprise Miner, DataDetective, Statistical Data Miner, Statistica, and Weka. You can read more about data mining on the Internet. But just to give you an idea, below are the steps in performing data mining: Define the objectives. This step is basically identifying why you need to perform data mining. What problem brought about a perceived data mining solution and what are the objectives for this project? Gather and organize the data. The bulk of the work in data mining is data gathering and exploring. Data has to be organized in an efficient and effective way for you to be able to process the information properly. Select the data-mining task. There are four basic data mining techniques: classification, regression, clustering and association rule. Choose the ones appropriate to your objectives. Modeling. This is when you actually perform the data mining procedure. Search for patterns in the database by applying your selected data mining techniques in order to create models. Data interpretation and validation. After the actual data mining task, the data gathered is now interpreted, validated, transformed and visualized using statistical techniques. Data deployment. This step can involve a report that is generated showing the patterns found in the data mining activity or the use of the data model on a larger group of data for further analysis. Data mining is an iterative process so you may have to go through several of the steps above a number of times until the results you derive answer your objectives. There was a time when data mining was not widely used by businesses. Now, public and private companies and organizations find data mining an invaluable way for them to keep up and even get ahead of their competitors. Businesses are now able to monitor the kind of customers their products cater to and what their customers’ buying behaviors are. The information mined and modeled from various types of databases is used for competition analysis, market research, economic trending, consumer behavior, industry research, geographical information analysis and so on. Even the FBI and other law enforcement groups use data mining techniques.
Views: 2 How to : Tips and Trick
EO Open Science 2.0 Poster space 28 Session B2 Marius Appel, University of Muenster
Views: 127 EO Open Science
In this demonstration, you learn how to apply a transformation to your model using Oracle SQL Developer Data Modeler Release 3.1. Copyright © 2012 Oracle and/or its affiliates. Oracle® is a registered trademark of Oracle and/or its affiliates. All rights reserved. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the "Materials"). The Materials are provided "as is" without any warranty of any kind, either express or implied, including without limitation warranties of merchantability, fitness for a particular purpose, and non-infringement.
Views: 1274 Oracle Learning Library
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 43467 nptelhrd
In this video, Billy Decker of StatSlice Systems shows you how to create and read a Classification Matrix in 5 minutes with the Microsoft Excel data mining add-in*. In this example, we will create a Classification Matrix based on a mining structure with all of its associated models that we have created previously. For the example, we will be using a tutorial spreadsheet that can be found on Codeplex at: https://dataminingaddins.codeplex.com/releases/view/87029 You will also need to attach the AdventureworksDW2012 data file to SQL Server which can be downloaded here: http://msftdbprodsamples.codeplex.com/releases/view/55330 *This tutorial assumes that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server with Analysis Services to which you have access rights.
Views: 4243 StatSlice Systems
Michele Pasin, Research Associate, Kings College, London Presented at "Representing Knowledge in the Digital Humanities", University of Kansas, September 24, 2011 Institute for Digital Research in the Humanities: http://idrh.ku.edu Abstract: Structured Prosopography provides a formal model for representing prosopography: a branch of historical research that traditionally has focused on the identification of people that appear in historical sources. Pre-digital print prosopographies, such as Martindale 1992, presented its materials as narrative articles about the individuals it contains. Since the 1990s, KCL's Department of Digital Humanities (formerly known as Center for Computing in the Humanities) has been involved in the development of structured prosopographical databases, and has had direct involvement in Prosopographies of the Byzantine World (PBE and PBW), Anglo-Saxon England (PASE), Medieval Scotland (PoMS) and now more generally northern Britain ("Breaking of Britain": BoB), and is currently in discussions about others. DDH has been involved in the development of a general "factoid-oriented" model of structure that although downplaying or eliminating narratives about people, has to a large extent served the needs of these various projects quite well.
Views: 439 University of Kansas IDRH
Even though exploring data visually is an integral part of the data analytic pipeline, we struggle to visually explore data once the number of dimensions go beyond three. This talk will focus on showcasing techniques to visually explore multi dimensional data p 3. The aim would be show examples of each of following techniques, potentially using one exemplar dataset. Standard 2D/3D Approaches Aesthetics e.g. Color, Size, Shape Small Multiples e.g. Trellis / Facets Matrices Views e.g. SPLOMs 3D Scatterplot Geometric Transformation Approaches Alternate Coordinates e.g. Parallel, Star Projections e.g. Dimensionality Reduction Tablelens Glyph based Approaches Star glyphs Stick Figures Pixel based Approaches Pixel bar charts Space filling curves Stacked based Approaches Dimensional Stacking Hierarchical Axis Treemaps The talk will also explore the role of interaction approaches to enhance our ability to visually explore the multi dimensional data. Interactive Approaches Navigation - Pan, Zoom, Scale, Rotate Selection & Annotation Filtering - Highlighting, Brushing and Linking Layering Dynamic Queries
Views: 3115 HasGeek TV
Part One of A Mathematical Theory of Knowledge: The Knowledge Engine - Machine Learning, Knowledge Extraction And Blockchain Live at The Humboldt Institut für Internet und Gesellschaft for the Blockchain for Science Hackathon. In cooperation with The Living Knowledge Network Foundation. Using mathematics such as algebras, information theory, graph theory, homomorphic encryption, algebraic information theory, domain theory, local computation, Markov Trees, linear algebra, many types of machine learning algorithms, fuzzy logic, many of which overlap, and even blockchain like structures, it can be shown that knowledge can be defined using three measures and extracted from datasets or other recorded observations, when a measure preserving mapping is found or an approximation thereof to form a type of informational compression which can be defined as knowledge. The first lecture on this topic introduces the concept of a decentralized, trusted, public/private knowledge engine which makes use of the aforementioned methods to classify and extract knowledge and link the granules of knowledge together with inferred causality, creating a knowledge base that can be stored and processed on a blockchain like structure. Many previous and current machine learning algorithms can be improved upon and even shown to be equivalent using a mathematical theory of knowledge. Thus more computational expensive methods of machine learning can be avoided, especially once any possible local computation is factored in, and algorithms that still have to be run that are more computational expensive would only have to be run once before the knowledge could be extracted as a measure preserving mapping. This can apply to algorithms such as clustering algorithms, or neural networking such as support vector machines and even deep learning.
Views: 604 The Amateur Academic
Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 25607 GoogleTechTalks
Original version is: http://togotv.dbcls.jp/ja/20160411.html RDF summit for individual genomics (The 2nd RDF summit in 2016) was held in Tohoku Medical Megabank Organization(ToMMo) in Sendai, Miyagi, Japan. On the first day of the meeting (19 Feb 2016), an informal seminar was held. The goal of this session is to briefly understand the technologies behind -- share the state of the art results and remaining issues on the reference genome graph, match maker exchange, semantic web genome databases etc. In this talk, Jouni Sirén makes a presentation entitled "Indexing Graphs for Path Queries". (15:09)
Views: 315 togotv
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 96 Clickmyproject
Google Tech Talk February 11, 2010 ABSTRACT Presented by Dr. Andy Yoo, Lawrence Livermore National Laboratory. Graphs have gained a lot of attention in recent years and have been a focal point in many emerging disciplines such as web mining, computational biology, social network analysis, and national security, just to name a few. These so-called scale-free graphs in the real world have very complex structure and their sizes already have reached unprecedented scale. Furthermore, most of the popular graph algorithms are computationally very expensive, making scalable graph analysis even more challenging. To scale these graph algorithms, which have different run-time characteristics and resource requirements than traditional scientific and engineering applications, we may have to adopt vastly different computing techniques than the current state-of-art. In this talk, I will discuss some of the findings from our studies on the performance and scalability of graph algorithms on various computing environments at LLNL, hoping to shed some light on the challenges in scaling large graph algorithms. Andy Yoo is a computer scientist in the Center for Applied Scientific Computing (CASC). His current research interests are scalable graph algorithms, high performance computing, large-scale data management, and performance evaluation. He has worked on the large graph problems since 2004. In 2005, he developed a scalable graph search algorithm and demonstrated it by searching a graph with billions of edges on IBM BlueGene/L, then the largest and fastest supercomputer. Andy was nominated for 2005 Gordon Bell award for this work. He is currently working on finding right combination of architecture, systems, and programming model to run large graph algorithms. Andy earned his Ph.D. degree in Computer Science and Engineering from the Pennsylvania State University in 1998. He joined LLNL in 1998. Andy is a member of the ACM, IEEE and the IEEE Computer Society, and SIAM.
Views: 19057 GoogleTechTalks
Google Tech Talk March 11, 2010 ABSTRACT Presented by Tina Eliassi-Rad. We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework ), can effectively incorporate hints from a number of other community detection algorithms and produce results that outperform the constituent parts. We describe two HCDF-based approaches which are: (1) effective, in terms of link prediction performance and robustness to small perturbations in network structure; (2) consistent, in terms of effectiveness across various application domains; (3) scalable to very large graphs; and (4) nonparametric. Our extensive evaluation on a collection of diverse and large real-world graphs, with millions of links, show that our HCDF-based approaches (a) achieve up to 0.22 improvement in link prediction performance as measured by area under ROC curve (AUC), (b) never have an AUC that drops below 0.91 in the worst case, and (c) find communities that are robust to small perturbations of the network structure as defined by Variation of Information (an entropy-based distance metric). Dr. Tina Eliassi-Rad, Lawrence Livermore National Laboratory http://people.llnl.gov/eliassirad1 Tina Eliassi-Rad (http://eliassi.org) is a computer scientist and principal investigator at the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. She will join the faculty at the Department of Computer Science at Rutgers University in Fall 2010. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research interests include data mining, machine learning, and artificial intelligence. Her work has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, and complex networks. She serves as an action editor for the Data Mining and Knowledge Discovery Journal.
Views: 4373 GoogleTechTalks
Subscribe To My YouTube Channel 5 Minutes Engineering http://www.youtube.com/c/5MinutesEngineering 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 12530 5 Minutes Engineering
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 5698 5 Minutes Engineering
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 89 myproject bazaar
To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A Unified Framework Recovering images from corrupted observations is necessary for many real-world applications. In this paper, we propose a unified framework to perform progressive image recovery based on hybrid graph Laplacian regularized regression. We first construct a multiscale representation of the target image by Laplacian pyramid, then progressively recover the degraded image in the scale space from coarse to fine so that the sharp edges and texture can be eventually recovered. On one hand, within each scale, a graph Laplacian regularization model represented by implicit kernel is learned, which simultaneously minimizes the least square error on the measured samples and preserves the geometrical structure of the image data space. In this procedure, the intrinsic manifold structure is explicitly considered using both measured and unmeasured samples, and the nonlocal self-similarity property is utilized as a fruitful resource for abstracting a priori knowledge of the images. On the other hand, between two successive scales, the proposed model is extended to a projected high-dimensional feature space through explicit kernel mapping to describe the interscale correlation, in which the local structure regularity is learned and propagated from coarser to finer scales. In this way, the proposed algorithm gradually recovers more and more image details and edges, which could not been recovered in previous scale. We test our algorithm on one typical image recovery task: impulse noise removal. Experimental results on benchmark test images demonstrate that the proposed method achieves better performance than state-of-the-art algorithms.
Views: 219 jpinfotechprojects
Mingyan Liu, Professor of Electrical Engineering and Computer Science (http://web.eecs.umich.edu/~mingyan/) gives the lecture "Confessions of a Pseudo Data Scientist" at the Women in Data Science Conference hosted by MIDAS (http://midas.umich.edu/). Dr. Liu’s research interests include optimal resource allocation, sequential decision theory, incentive design, and performance modeling and analysis, all within the context of communications networks. Her most recent research involves online learning, modeling and mining of large-scale internet measurement data concerning cyber-security, and incentive mechanisms for interdependent security games. For more lectures on demand, visit the Alumni Engagement website: http://www.engin.umich.edu/college/info/alumni/professional-dev/lectures
Views: 837 Michigan Engineering
Dr. Soper gives a lecture on big data, data warehouses, and business intelligence systems. Topics covered include big data, the NoSQL movement, structured storage, the MapReduce process, the Apache Cassandra data model, data warehouse concepts, multidimensional databases, business intelligence (BI) concepts, and data mining,
Views: 82131 Dr. Daniel Soper
Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training...... ---------------------------------------------------------------- JAVA & .NET PROJECTS: Networking, Network Security, Data Mining, Cloud Computing, Grid Computing, Web Services, Mobile Computing, Software Engineering, Image Processing, E-Commerce, Games App, Multimedia, etc., EMBEDDED SYSTEMS: Embedded Systems,Micro Controllers, DSC & DSP, VLSI Design, Biometrics, RFID, Finger Print, Smart Cards, IRIS, Bar Code, Bluetooth, Zigbee, GPS, Voice Control, Remote System, Power Electronics, etc., ROBOTICS PROJECTS: Mobile Robots, Service Robots, Industrial Robots, Defence Robots, Spy Robot, Artificial Robots, Automated Machine Control, Stair Climbing, Cleaning, Painting, Industry Security Robots, etc., MOBILE APPLICATION (ANDROID & J2ME): Android Application, Web Services, Wireless Application, Bluetooth Application, WiFi Application, Mobile Security, Multimedia Projects, Multi Media, E-Commerce, Games Application, etc., MECHANICAL PROJECTS: Auto Mobiles, Hydraulics, Robotics, Air Assisted Exhaust Breaking System, Automatic Trolley for Material Handling System in Industry, Hydraulics And Pneumatics, CAD/CAM/CAE Projects, Special Purpose Hydraulics And Pneumatics, CATIA, ANSYS, 3D Model Animations, etc., CONTACT US: ECWAY TECHNOLOGIES 15/1 Sathiyamoorthi Nagar, 2nd Cross, Thanthonimalai(Opp To Govt. Arts College) Karur-639 005. TamilNadu , India. Cell: +91 9894917187. Website: www.ecwayprojects.com | www.ecwaytechnologies.com Mail to: [email protected]
Views: 151 Ecway Karur
talk given at IMA London branch 1st November 2016 http://www.ima.org.uk/activities/branches/london.cfm.html Abstract: There are so many terms in the analytics industry today, and no one really understands them well: Data science, data analysis (or is it analytics?), data architecture, web analytics, business intelligence, and management information; there are also many platforms from relational databases to Hadoop clusters, and programming languages like python, .NET, C# and F# (sounds like music!); not to mention all the statistical packages like (R, SPSS and STATA) and front end tools (like Pentaho, D3.js or Tableau). But what does this mean? Where do all of these elements fit in the data and analytics profession? In this talk I will explain what they are, how they work and how they interact with each other, but most importantly how businesses use them to take advantage of all these mathematical tools and make data-driven decisions. I will give real life examples on these tools and methods like machine learning, data mining and mathematical modelling and also how platforms are used. Dr. Plata did his doctoral research at Imperial College London before taking up a post as a research fellow at the University of Exeter. Prior to that, he did a degree and masters in mathematics, specializing firstly on differential and algebraic topology and moving then into spectral theory, homeomorphic dynamics and ergodic theory which classically fall into the applied fields like optimisation, game theory, and machine learning. Among other publications, he wrote a book under Peter Lang Publishers titled "Visions of Applied Mathematics" In terms of mathematical applications, Dr. Plata has extensive experience building, and developing analytics programmes as well as leading data projects and data science teams from FTSE100 companies to technology SMEs. He has worked mainly in the retail space including digital and mobile, pioneering on behaviour analytics, machine learning and big data. He is a fellow of the IMA and he currently heads the 'data science and advanced analytics' team at Home Retail.
Views: 492 IMAmaths
DataEDGE 2013 - http://dataedge.ischool.berkeley.edu Data Just Right: A Practical Introduction to Data Science Skills Michael Manoochehri, Developer Programs Engineer, Google This will be a introductory session covering current tools, skills, and trends in data analysis. Using a hands-on walkthrough with a real data set, we'll take a look a common data use cases and patterns, along with the technology most appropriate for solving some of these data challenges. We'll also take a look at trends in technology, learn which Data Scientist tasks are becoming automated, and which tasks require human skills more than ever!
Views: 29640 Berkeley School of Information
ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2013 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html IEEE 2013 Dot Net: http://www.chennaisunday.com/ieee-2012-java-projects.html IEEE 2012 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html IEEE 2012 Dot Net: http://www.chennaisunday.com/ieee-2012-projects.html IEEE 2011 JAVA: http://www.chennaisunday.com/ieee-2011-java-projects.html IEEE 2011 DOT NET: http://www.chennaisunday.com/ieee-2011-projects.html IEEE 2010 JAVA: http://www.chennaisunday.com/ieee-2010-java-projects.html IEEE 2010 DOT NET: http://www.chennaisunday.com/ieee-2010-dotnet-projects.html Real Time APPLICATION: http://www.chennaisunday.com/softwareprojects.html Contact: 9566137117/ 044-42046569 -- *Contact * * P.Sivakumar MCA Director ChennaiSunday Systems Pvt Ltd Phone No: 09566137117 New No.82, 3rd Floor, Arcot Road, Kodambakkam, Chennai - 600 024. URL: www.chennaisunday.com Location: http://www.chennaisunday.com/mapview.html
Views: 2631 Shiva Kumar
This session gives an introduction to machine learning for developers who are new to data science, and it shows how to build end-to-end MLlib Pipelines in Apache Spark. It provides example code to personalize recommendations, score inbound leads, or do natural language processing in Scala and Python. See how to productionize machine learning pipelines to create richer, more useful applications.
Views: 4307 Microsoft Visual Studio
Views: 3896 LiveLessons
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Views: 34 RANJITH KUMAR
Besides social networks in our daily lives such as Facebook or Twitter, networks now play an increasingly important role in both academic research and industry. Network analysis has become an indispensable toolkit as more and more relational data are made available. In this video, F. Bill Shi of Knowledge Lab reviews the state of the art of modern network analysis, and provides a hands-on tutorial on three problems that have found successful applications in the real world (one of which has made billions of dollars): how to rank nodes, how to identify meaningful clusters of nodes, and how to predict potential links in a network. To follow along with this workshop, download the iPython notebook and datasets at: https://github.com/KnowledgeLab/CI-Workshop-Networks Python 2.7, igraph (http://igraph.org/python/#startpy), and gephi (http://gephi.github.io/) are also recommended for this tutorial. Discovery Engines: Under The Hood Recent years have brought an explosion of software and tools for working with data, making once arduous tasks such as processing, analysis, machine learning, and visualization easy and accessible. But as the data science toolbox grows larger and larger, it becomes harder to find the proper tool for the job at hand, not to mention how to get the most out of its use. "Discovery Engines: Under the Hood" is a new monthly workshop series organized by the Computation Institute, offering practical, hands-on instruction with new and popular computational tools. Learn from experienced users at CI research centers about programming libraries, web platforms, visualization software, and analytic techniques that can make a difference in your own projects.
Views: 970 Computation Institute
The solution for distance and continuous learning, based on social media technologies. Social and collaborative Portal: • collaborative environment • networking • blogs and forums • suggestions and recommendations Learning Management system • Cross media e-learning • collective analysis • user behaviour analysis Intelligent content model • complex interactive content • multimedia annotations • personal collection and playlist Access with different devices • everywhere you are • whenever you like • with any device real application on: http://www.eclap.eu -- http://mobmed.axmedis.org Innovation Selected "Italia degli innovatori" Italy of innovators: http://www.disit.dsi.unifi.it/projects.html
Views: 105 Paolo Nesi
In this video from the Washington DC Graph Tour, Ryan Boyd (Engineer and Director of DevRel at Neo4j) discusses the evolution of Graph Algorithms on Neo4j and other platforms. Ryan discusses a variety of algorithms - from shortest paths to PageRank to centrality to clustering and community detection. Whether you're doing urban planning, logistics, anti money laundering, or building recommendation engines, Neo4j Graph Algorithms will help you learn more about your data relationships, quickly.
Views: 207 Neo4j
Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Hazy: Making Data-driven Statistical Applications Easier to build and Maintain by Chris Re Christopher (Chris) Ré is currently an assistant professor in the department of Computer Sciences at the University of Wisconsin-Madison. The goal of his work is to enable users and developers to build applications that more deeply understand data. In many applications, machines can only understand the meaning of data statistically, e.g., user-generated text or data from sensors. Abstract: The main question driving my group's research is: how does one deploy statistical data-analysis tools to enhance data driven systems? Our goal is to find abstractions that one needs to deploy and maintain such systems. In this talk, I describe my group's attack on this question by building a diverse set of statistical-based data-driven applications: a system whose goal is to read the Web and answer complex questions, a muon detector in collaboration with a neutrino telescope called IceCube, and a social-science applications involving rich content (OCR and speech data). Even in this diverse set, my group has found common abstractions that we are exploiting to build and to maintain systems. Of particular relevance to this workshop is that I have heard of applications in each of these domains referred to as "big data." Nevertheless, in our experience in each of these tasks, after appropriate preprocessing, the relevant data can be stored in a few terabytes -- small enough to fit entirely in RAM or on a handful of disks. As a result, it is unclear to me that scale is the most pressing concern for academics. I argue that dealing with data at TB scale is still challenging, useful, and fun, and I will describe some of our work in this direction. This is joint work with Benjamin Recht, Stephen J. Wright, and the Hazy Team
Views: 2681 GoogleTechTalks
"Analyzing Social Media Networks with NodeXL" When information visualization is smoothly integrated with statistical techniques users can make important discoveries and bold decisions. Our 20-year history in coupling direct manipulation principles with dynamic queries, coordinated multiple windows, tree-maps, time-box selectors, and other innovations has produced academic and commercial success stories such as www.Spotfire.com and www.cs.umd.edu/hcil/treemap-history. Now we've turned to the difficult problem of network analysis and visualization. The free, open-source NodeXL (www.codeplex.com/nodexl) demonstrates novel approaches to importing network data (email, website, Facebook, Twitter, Flickr, etc.), applying metrics, performing clustering, and then giving rich controls over network layouts to support exploration and presentation. BEN SHNEIDERMAN (http://www.cs.umd.edu/~ben) is a Professor in the Department of Computer Science and Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://www.cs.umd.edu/hcil/) at the University of Maryland. He was elected as a Fellow of the Association for Computing (ACM) in 1997, a Fellow of the American Association for the Advancement of Science (AAAS) in 2001, and a Member of the National Academy of Engineering in 2010. He received the ACM SIGCHI Lifetime Achievement Award in 2001.
Views: 4156 Talks at Google
Robotic systems are revolutionizing applications from transportation to health care. However, many of the characteristics that make robots ideal for future applications—such as autonomy, self-learning, and knowledge sharing—also raise concerns about the evolution of the technology. Blockchain, an emerging technology that originated in the digital currency field, shows great potential to make robotic operations more secure, autonomous, flexible, and even profitable, thereby bridging the gap between purely scientific domains and real-world applications. This symposium seeks to move beyond the classical view of robotic systems to advance our understanding about the possibilities and limitations of combining state-of-the art robotic systems with blockchain technology. More information at: https://www.media.mit.edu/events/symposium-on-blockchain-for-robotics/ License: CC-BY-4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
Views: 2426 MIT Media Lab