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DATA MINING CONCEPTS AND TECHNIQUES
 
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Data Mining
Views: 169 ARUN RAJ M
Lecture 16. SIMD Processing (Vector Processors) - CMU - Computer Architecture 2014 - Onur Mutlu
 
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Lecture 16. SIMD Processing (Vector and Array Processors) Lecturer: Prof. Onur Mutlu (http://users.ece.cmu.edu/~omutlu/) Date: Feb 24th, 2014 Lecture 16 slides (pdf): http://www.ece.cmu.edu/~ece447/s14/lib/exe/fetch.php?media=onur-447-spring14-lecture16-simd-afterlecture.pdf Lecture 16 slides (ppt): http://www.ece.cmu.edu/~ece447/s14/lib/exe/fetch.php?media=onur-447-spring14-lecture16-simd-afterlecture.pptx Course webpage: http://www.ece.cmu.edu/~ece447/s14/doku.php?id=start Module materials: http://www.ece.cmu.edu/~ece447/s14/doku.php?id=schedule
Design of Digital Circuits - Lecture 5: Combinational Logic (ETH Zürich, Spring 2018)
 
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Design of Digital Circuits, ETH Zürich, Spring 2018 (https://safari.ethz.ch/digitaltechnik/) Lecture 5: Combinational Logic Lecturer: Professor Onur Mutlu (http://people.inf.ethz.ch/omutlu) Date: March 8, 2018 Slides (ppt): https://safari.ethz.ch/digitaltechnik/spring2018/lib/exe/fetch.php?media=onur-digitaldesign-2018-lecture5-combinational-logic-afterlecture.pptx Slides (pdf): https://safari.ethz.ch/digitaltechnik/spring2018/lib/exe/fetch.php?media=onur-digitaldesign-2018-lecture5-combinational-logic-afterlecture.pdf
Views: 2185 Onur Mutlu Lectures
Introduction to Elemental Analysis by ED-XRF (Justin Masone)
 
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For more information, visit https://nanohub.org/resources/22621 Justin Masone 6/3/15 Introduction to Elemental Analysis by ED-XRF
Views: 8979 NanoBio Node
Big Data PPT
 
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PPT on “Big Data” is data whose scale, diversity and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it. #Bigdata #Dataprocessing #technology Visit: https://www.topicsforseminar.com to Download the Big Data PPT for computer science engineering seminar
Views: 36029 Topics For Seminar
3D Animated Powerpoint Templates Free Download
 
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3D Animated Powerpoint Templates Free Download ► Infographic: http://bit.ly/Tiny-PPT ► Brochure: http://bit.ly/Tiny-Brochure ► Newspaper: http://bit.ly/Tiny-Newspaper ------------------------------------------------- ► Subscribe: http://bit.ly/Tiny-PPT-Subscribe ► Pinterest: http://bit.ly/TinyPPT-Pinterest ► Donation: http://bit.ly/Tiny-PPT-Donation ------------------------------------------------- #TinyPPT 💪 An online service about design and provide free Modern Business Infographic & Diagram for PowerPoint with High-quality editable graphics, easily customizable to your needs and include animated. Can be used for presentation, banner, report, brochure, workflow layout, diagram, number options, web design, infographics. Work right away in MS PowerPoint, Keynote, Google Drive, OneDrive,... Export to a lot format such as PDF, MP4, PNG, JPG,... ------------------------------------------------- Interesting Playlists: ► Intro Slides with Video Effects: http://bit.ly/Intro-Slides-TinyPPT ► 3D Infographics and Diagrams: http://bit.ly/3D-Infographics-PowerPoint ► 2D Infographics and Diagrams: http://bit.ly/Infographic-Diagrams ► Editable Newspaper Template: http://bit.ly/Newspaper-Template ► Brochure Template Word: http://bit.ly/Brochure-Template ► Google Slides Tutorial: http://bit.ly/Google-Slides
Views: 464061 TinyPPT
FP tree tabi pptx
 
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Views: 230 taban osman
R Tutorial - from coursera
 
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The structure of a data analysis (steps in the process, knowing when to quit, etc.) Types of data (census, designed studies, randomized trials) Types of data analysis questions (exploratory, inferential, predictive, etc.) How to write up a data analysis (compositional style, reproducibility, etc.) Obtaining data from the web (through downloads mostly) Loading data into R from different file types Plotting data for exploratory purposes (boxplots, scatterplots, etc.) Exploratory statistical models (clustering) Statistical models for inference (linear models, basic confidence intervals/hypothesis testing) Basic model checking (primarily visually) The prediction process Study design for prediction Cross-validation A couple of simple prediction models Basics of simulation for evaluating models Ways you can fool yourself and how to avoid them (confounding, multiple testing, etc.)
Views: 1198 Anand Maurya
MIT CompBio Lecture 06 - Gene Expression Analysis: Clustering and Classification
 
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MIT Computational Biology: Genomes, Networks, Evolution, Health Prof. Manolis Kellis http://compbio.mit.edu/6.047/ Fall 2018 Lecture 6- Gene expression analysis: Clustering and Classification 1. Introduction to gene expression analysis - Technology: microarrays vs. RNAseq. Resulting data matrices - Supervised (Clustering) vs. unsupervised (classification) learning 2. K-means clustering (clustering by partitioning) - Algorithmic formulation: Update rule, optimality criterion. Fuzzy k-means. - Machine learning formulation: Generative models, Expectation Maximization. 3. Hierarchical Clustering (clustering by agglomeration) - Basic algorithm, Distance measures. Evaluating clustering results 4. Naïve Bayes classification (generative approach to classification) - Discriminant function: class priors, and class-conditional distributions - Training and testing, Combine mult features, Classification in practice 5. (optional) Support Vector Machines (discriminative approach) - SVM formulation, Margin maximization, Finding the support vectors - Non-linear discrimination, Kernel functions, SVMs in practice Slides for Lecture 6: https://stellar.mit.edu/S/course/6/fa18/6.047/courseMaterial/topics/topic2/lectureNotes/Lecture06_ExpressionClust---ication4_thin.pptx/Lecture06_ExpressionClust---Classification_6up.pdf
Views: 719 Manolis Kellis
28c3 -  Datamining for Hackers
 
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This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net 28c3 - Datamining for Hackers http://events.ccc.de/congress/2011/Fahrplan/attachments/1985_CCC.pptx This talk presents Traffic Mining (TM) particularly in regard to VoiP applications such as Skype. TM is a method to digest and understand large quantities of data. Voice over IP (VoIP) has experienced a tremendous growth over the last few years and is now widely used among the population and for business purposes. The security of such VoIP systems is often assumed, creating a false sense of privacy. Stefan will present research into leakage of information from Skype, a widely used and protected VoIP application. Experiments have shown that isolated phonemes can be classified and given sentences identified. By using the dynamic time warping (DTW) algorithm, frequently used in speech processing, an accuracy of 60% can be reached. The results can be further improved by choosing specific training data and reach an accuracy of 83% under specific conditions
Views: 201 SecurityTubeCons
Creative Decision Tree Diagram in PowerPoint
 
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Creative Decision Tree Diagram in PowerPoint Follow this step by step screencast tutorial for a simple decision tree diagram to use in your next presentation. Key Links: ********** 25 Creative Ideas MINI Training http://www.presentation-process.com/25-creative-presentation-ideas-mini-training.html Comprehensive All In One Bundle - PowerPoint Templates: http://www.presentation-process.com/comprehensive-all-in-one-powerpoint-bundle.html Ramgopal's PowerPoint Mastery Training Program: http://www.presentation-process.com/ramgopals-powerpoint-mastery-program.html
Views: 5255 Presentation Process
Lecture 02 - Is Learning Feasible?
 
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Is Learning Feasible? - Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 5, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 340746 caltech
X-Ray Fluorescence Spectroscopy (XRF) Explained - Elemental Analysis Technique
 
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X-ray fluorescence spectroscopy (XRF) is one of the most common techniques used for studying the elemental composition of different materials. In this materials characterization method the sample is irradiated with x-ray radiation, which knocks out electrons from atoms, leaving them in an excited state. During the relaxation of these atoms the excess energy is released in the form of x-ray radiation. The energy and intensity of this radiation however depends directly on the composition of the material. Therefore it is possible to study a materials composition by detecting the x-rays that come out of the sample.
Views: 36862 Captain Corrosion
Machine Learning in Materials Science
 
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Presentation made by Prof. Ramprasad at an IPAM workshop in UCLA (September 2016)
Views: 4115 Rampi Ramprasad
Understanding Wavelets, Part 1: What Are Wavelets
 
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This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts can be applied to 2D data such as images. Video Transcript: Hello, everyone. In this introductory session, I will cover some basic wavelet concepts. I will be primarily using a 1-D example, but the same concepts can be applied to images, as well. First, let's review what a wavelet is. Real world data or signals frequently exhibit slowly changing trends or oscillations punctuated with transients. On the other hand, images have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data, both perceptually and in terms of the information they provide. The Fourier transform is a powerful tool for data analysis. However, it does not represent abrupt changes efficiently. The reason for this is that the Fourier transform represents data as sum of sine waves, which are not localized in time or space. These sine waves oscillate forever. Therefore, to accurately analyze signals and images that have abrupt changes, we need to use a new class of functions that are well localized in time and frequency: This brings us to the topic of Wavelets. A wavelet is a rapidly decaying, wave-like oscillation that has zero mean. Unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. Wavelets come in different sizes and shapes. Here are some of the well-known ones. The availability of a wide range of wavelets is a key strength of wavelet analysis. To choose the right wavelet, you'll need to consider the application you'll use it for. We will discuss this in more detail in a subsequent session. For now, let's focus on two important wavelet transform concepts: scaling and shifting. Let' start with scaling. Say you have a signal PSI(t). Scaling refers to the process of stretching or shrinking the signal in time, which can be expressed using this equation [on screen]. S is the scaling factor, which is a positive value and corresponds to how much a signal is scaled in time. The scale factor is inversely proportional to frequency. For example, scaling a sine wave by 2 results in reducing its original frequency by half or by an octave. For a wavelet, there is a reciprocal relationship between scale and frequency with a constant of proportionality. This constant of proportionality is called the "center frequency" of the wavelet. This is because, unlike the sinewave, the wavelet has a band pass characteristic in the frequency domain. Mathematically, the equivalent frequency is defined using this equation [on screen], where Cf is center frequency of the wavelet, s is the wavelet scale, and delta t is the sampling interval. Therefore when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. For instance, here is how a sym4 wavelet with center frequency 0.71 Hz corresponds to a sine wave of same frequency. A larger scale factor results in a stretched wavelet, which corresponds to a lower frequency. A smaller scale factor results in a shrunken wavelet, which corresponds to a high frequency. A stretched wavelet helps in capturing the slowly varying changes in a signal while a compressed wavelet helps in capturing abrupt changes. You can construct different scales that inversely correspond the equivalent frequencies, as mentioned earlier. Next, we'll discuss shifting. Shifting a wavelet simply means delaying or advancing the onset of the wavelet along the length of the signal. A shifted wavelet represented using this notation [on screen] means that the wavelet is shifted and centered at k. We need to shift the wavelet to align with the feature we are looking for in a signal.The two major transforms in wavelet analysis are Continuous and Discrete Wavelet Transforms. These transforms differ based on how the wavelets are scaled and shifted. More on this in the next session. But for now, you've got the basic concepts behind wavelets.
Views: 183663 MATLAB
Data Mining, Лекция №1
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Алгоритмы интеллектуальной обработки больших объемов данных", Лекция №1 - "Задачи Data Mining" Лектор - Николай Анохин Обзор задач Data Mining. Стандартизация подхода к решению задач Data Mining. Процесс CRISP-DM. Виды данных. Кластеризация, классификация, регрессия. Понятие модели и алгоритма обучения. Слайды лекции: http://www.slideshare.net/Technosphere1/lecture-1-47107550 Другие лекции курса Data Mining | https://www.youtube.com/playlist?list=PLrCZzMib1e9pyyrqknouMZbIPf4l3CwUP Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
Updated And Upgraded Physical Components Of A Data Warehouse
 
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Download this free Updated And Upgraded Physical Components Of A Data Warehouse Powerpoint here: https://theartofservice.com Complete Toolkit at https://store.theartofservice.com/itil.html As part of its scope, a second presentation is available to introduce Data Analytics and Data Mining, which is related to the second step of Business Intelligence.
Views: 26 TheArtofService
FP Tree මට තේරුණ විදිහට... :D
 
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Question : http://www.mediafire.com/view/myfiles/#bhw9896c9y8fyjn Answer File : http://www.mediafire.com/view/hts98wys0cvyxfs/FP-Tree.xlsx
Views: 2239 Sithira Pathmila
Лекция 2.1 - Softmax
 
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Слайды: https://www.dropbox.com/s/sxj3wqzrep4p93x/Lecture%202%20-%20Linear%20Classifier%20-%20Softmax.pptx?dl=0
Views: 6039 sim0nsays
MS PowerPoint 2007 Tutorial in Hindi / Urdu : Introduction - 1
 
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MS PowerPoint 2007 Playlist - https://goo.gl/FK9y7O इस विडियो में आप जानेगे Microsoft PowerPoint 2007 का परिचय In this video you will get to know about the introduction of Microsoft PowerPoint 2007.
Views: 746731 Gyanyagya
Tutorial_Algoritma_Assosiasi_FP-Growth_DMAB_UNIPDU_2016
 
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cara perhitungan algoritma assosiasi menggunakan assosiasi FP-Growth
Views: 515 suhairini rusframs
Knowledge discovery and data mining in pharmaceutical cancer research (KDD 2011)
 
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Knowledge discovery and data mining in pharmaceutical cancer research KDD 2011 Paul Rejto Biased and unbiased approaches to develop predictive biomarkers of response to drug treatment will be introduced and their utility demonstrated for cell cycle inhibitors. Opportunities to leverage the growing knowledge of tumors characterized by modern methods to measure DNA and RNA will be shown, including the use of appropriate preclinical models and selection of patients. Furthermore, techniques to identify mechanisms of resistance prior to clinical treatment will be discussed. Prospects for systematic data mining and current barriers to the application of precision medicine in cancer will be reviewed along with potential solutions.
Azure SQL Data Warehouse
 
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SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data. Use SQL Data Warehouse as a key component of a big data solution. Import big data into SQL Data Warehouse with simple PolyBase T-SQL queries, and then use the power of MPP to run high-performance analytics. As you integrate and analyze, the data warehouse will become the single version of truth your business can count on for insights. Here is the link to today's presentation: https://nzpowerlunchfiles.blob.core.windows.net/data/azure-sql-data-warehouse-11-16-2018.pptx
Views: 232 Azure Power Lunch
Combining Multiple PowerPoint Presentations into One Slide Deck for Mac
 
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If you want to combine a PowerPoint presentation with another one that you’ve previously made, this is a fairly easy trick. It’s simple to import another existing presentation into the one that you’re currently working on. To read the full article, visit this link: https://www.bettercloud.com/monitor/the-academy/how-to-combine-multiple-powerpoint-presentations-into-one-slide-deck/
Views: 65623 BetterCloud
Oracle's Machine Learning & Advanced Analytics 12.2 & Oracle Data Miner 4.2 New Features
 
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Oracle's Machine Learning and Advanced Analytics 12.2 and Oracle Data Miner 4.2 New Features. This presentation highlights the new machine learning algorithms, features, functions and "differentiators" added to Oracle Database Release 12.2 and Oracle SQL Developer4.2. These features and functioned are "packaged" as part of the Oracle Advanced Analytics Database Option and Oracle Data Miner workflow UI on-premise and in the Oracle Database Cloud Service High and Extreme Editions. I hope you enjoy the video! Charlie Berger [email protected]
Views: 8939 Charlie Berger
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 430640 Thales Sehn Körting
Decision Tree
 
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Decision Tree for PowerPoint presentations. Get this graph at http://www.poweredtemplate.com/powerpoint-diagrams-charts/ppt-tree-diagrams/00040/0/index.html Download creative, pre-made, and complete editable diagrams, shapes, icons and charts at http://www.PoweredTemplate.com
Views: 307 PoweredTemplate.com
Decision Tree 3: which attribute to split on?
 
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Full lecture: http://bit.ly/D-Tree Which attribute do we select at each step of the ID3 algorithm? The attribute that results in the most pure subsets. We can measure purity of a subset as the entropy (degree of uncertainty) about the class within the subset.
Views: 183729 Victor Lavrenko
Textual Analysis PowerPoint
 
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The overview and findings of my textual analysis project for COM610.
Views: 490 Nikki Edmondson
Customer Lifecycle Management
 
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Utlimate Customer Lifecycle Management
Views: 3289 Pitney Bowes
Лекция 10 - Attention
 
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Слайды: https://www.dropbox.com/s/g7rc79sl8jeqw3q/Lecture%2010%20-%20Attention%20-%20annotated.pptx?dl=0
Views: 2849 sim0nsays
Portable X-Ray Fluorescence Lecture
 
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Portable X-Ray Fluorescence is a tool for detecting elemental composition of soil and water. This is normally a slow and expensive process, but necessary to detect heavy metals and salinity for soil research. The portable meter offers a way to measure metals and salinity in the field in less than 1 minute. The authors review the use of the meter for several applications. Dr. David Weindorf of Texas Tech University is a world-renowned expert in use of the meter and presented this lecture to faculty while visiting Dr. John Galbraith at Virginia Tech on April 12, 2017.
Views: 583 VA Extension
Assignment3 Group4 Cost Sensitive Learning
 
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Group reading assignment for COMP5138 See link of slides here https://www.dropbox.com/s/pyuabcis1gozgzq/COMP5318%20-%20The%20Foundations%20of%20Cost-Sensitive%20Learning%20v7.pptx
Views: 179 Imi Chitterman
From Data to Knowledge - 508 - Una-May O'Reilly
 
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Slides: http://lyra.berkeley.edu/CDIConf/pdfs/KnitPresentationLocal.pptx.pdf Una-May O'Reilly: "Knit: Integrating Large Scale Partial Cognitive Analyses of Data". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012). Abstract Una-May O'Reilly (MIT, CSAIL) Increasingly humans are becoming an integral part of knowledge discovery systems. Their ability to process information based on the context and their perception allows them to discern unique and novel patterns. However these systems are vulnerable to noisy and inconsistent behavior of humans.Additionally they are limited by human's cognitive capacity in processing data in real time. Our goal is to develop real-time knowledge discovery techniques which extend/accelerate/amplify the value of analyst's locally discerned patterns. Our approach knits together local similarity sets without explicit features. It also actively presents data to different analysts to effectively derive a consensus of global similarity.
Views: 487 ckleinastro
Young Statisticians Meeting 2014 Prize Winners
 
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Experts Sharing Knowledge through Wikipedia - Experiences at the Office for National Statistics Hannah Thomas, Office for National Statistics, UK Download slides: http://www/statslife.org.uk/files/RSS2014-slides/RSS2014-Hannah-Thomas.pptx Predicting the Results of the Scottish Referendum Zhou Fang, Biomathematics and Statistics Scotland, UK Download slides: http://www/statslife.org.uk/files/RSS2014-slides/RSS2014-Zhou-Fang.pdf
Views: 490 RoyalStatSoc
Introduction to R Shiny: Building web apps in R Shiny for learning and visualization
 
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Slides: http://files.meetup.com/1685538/IntroductionRShiny.pptx R Shiny, from the people behind R Studio, allows you to quickly and easily build basic web applications using only the R language. I will be demonstrating the basics of web app creation, and will show you a number of examples for purposes such as data visualization and student learning. The talk will require only rudimentary knowledge of R. After the talk (45mins) you are welcome to join me at the Colonial Hotel for dinner. Alec Stephenson is a CSIRO scientist and a former academic at The National University of Singapore and Swinburne University. It is his third talk for the MelbURN group, following previous talks on spatial data (Sept 2011) and speeding up your R code (Sept 2012). He has been writing R software since the days when there were only a hundred or so R packages. He still dislikes the ifelse function.
Views: 10767 Jeromy Anglim
Blood Biomarkers in the Management of Prostate Cancer - Tarek Bismar, MD
 
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Dr. Bismar, from the Southern Alberta Cancer Research Institute, leads a heady discussion on how innovations in examining blood and tissue-based molecular signatures can help predict prostate cancer progression. He talks about the different known molecular markers and their characteristics, prognostic gene signatures and the entire process from getting prostate samples to ultimately finding cDNA outcomes. To look closer at Dr. Bismar's graphs and information, the powerpoint can be downloaded in full here: http://pccncalgary.org/bismar_oct15.pptx
Views: 211 PROSTAID Calgary
F P Tree
 
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Views: 66 LeakyMeat
What's in There? Searching by Variable at ICPSR
 
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In this webinar recoring (originally broadcast on June 14, 2016), George Alter, Director of ICPSR, will demonstrate strategies for searching more than 4.5 million variable descriptions in ICPSR's Social Science Variables Database, including our new crosswalk between the American National Election Study and the General Social Survey. The ICPSR Website allows users to search for variables singly or in groups. The "Compare Variables" feature brings up question text, frequencies, universe and other information, and all searches are linked to ICPSR's dynamic online codebooks. The ICPSR variable search, supported by its thorough methods documentation, is an effective tool for those that are: • Searching for data with particular questions/content for analysis (for research papers/publishing). • Desiring to compare or harmonize data across projects. • Mining for questions to design research surveys and/or to teach survey design --Including the demonstration of the effect of question wording and answer categories on variable distributions and the changes (evolution) in question wording/response categories over time. • Desiring to deposit research data for curation to enhance data discovery, increase research impact, and demonstrate that federal data sharing requirements have been met. This recording will benefit research scientists, teaching faculty, students, and those assisting these individuals. To download presentation slides: http://www.icpsr.umich.edu/files/videos/Searching_ICPSR.pptx
Views: 254 ICPSR
SEO Best Practices Presentation at the Harvard Club in Boston, MA
 
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Many small and mid-sized companies face steep competition from industry conglomerates when it comes to online presence. For these companies, smart SEO improvements can transform a websites' performance. While bigger companies have teams devoted exclusively to managing their online image, smaller brands can leverage efficient targeted advertising to draw in their specific demographic, and edge out their competition. For instance, recognizing the key terms your customer base search for will identify which areas to emphasize on your website, and improve your appearance on the major search engines. With today's sophisticated market analytics, your existing customer data can be a cost-effective way to identify strengths and draw in targeted, interested leads.
Advanced Concepts in Blockchain Design (Lior Yaffe, July 2016)
 
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Lior Yaffe explains some design concepts that have made it into NXT and the upcoming Ardor platform. The lecture took place on 27.7.2016. Slides: https://bitcoil.co.il/files/Advanced.Concepts.in.Blockchain.Design.pptx http://prezi.com/x8jyard5-h51/ Event page: http://www.meetup.com/bitcoin-il/events/232620342/
Views: 4883 Bitcoin Israel
IEEE 2014 VLSI ULTRA HIGH THROUGHPUT LOW POWER pptx
 
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PG Embedded Systems www.pgembeddedsystems.com #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2014 ieee projects, 2014 ieee java projects, 2014 ieee dotnet projects, 2014 ieee android projects, 2014 ieee matlab projects, 2014 ieee embedded projects, 2014 ieee robotics projects, 2014 IEEE EEE PROJECTS, 2014 IEEE POWER ELECTRONICS PROJECTS, ieee 2014 android projects, ieee 2014 java projects, ieee 2014 dotnet projects, 2014 ieee mtech projects, 2014 ieee btech projects, 2014 ieee be projects, ieee 2014 projects for cse, 2014 ieee cse projects, 2014 ieee it projects, 2014 ieee ece projects, 2014 ieee mca projects, 2014 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2014 mtech projects, 2014 mphil projects, 2014 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2014 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2014 ieee omnet++ projects, ieee 2014 oment++ project, innovative ieee projects, latest ieee projects, 2014 latest ieee projects, ieee cloud computing projects, 2014 ieee cloud computing projects, 2014 ieee networking projects, ieee networking projects, 2014 ieee data mining projects, ieee data mining projects, 2014 ieee network security projects, ieee network security projects, 2014 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2014 wireless networking projects ieee, 2014 ieee web service projects, 2014 ieee soa projects, ieee 2014 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2014 IEEE java projects,2014 ieee Project Titles, 2014 IEEE cse Project Titles, 2014 IEEE NS2 Project Titles, 2014 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2014 - 2014 ... Image Processing. IEEE 2014 - 2014 Projects | IEEE Latest Projects 2014 - 2014 | IEEE ECE Projects2014 - 2014, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2014 IEEE C#, C Sharp Project Titles, 2014 IEEE EmbeddedProject Titles, 2014 IEEE NS2 Project Titles, 2014 IEEE Android Project Titles. 2014 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2014, IEEE 2014 PROJECT TITLES, M.TECH. PROJECTS 2014, IEEE 2014 ME PROJECTS.
IEEE 2014 EMBEDDED TELEMEDICINE WEB SERVER BASED ON EMBEDDED SERIAL DEVICE  pptx
 
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PG Embedded Systems www.pgembeddedsystems.com #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2014 ieee projects, 2014 ieee java projects, 2014 ieee dotnet projects, 2014 ieee android projects, 2014 ieee matlab projects, 2014 ieee embedded projects, 2014 ieee robotics projects, 2014 IEEE EEE PROJECTS, 2014 IEEE POWER ELECTRONICS PROJECTS, ieee 2014 android projects, ieee 2014 java projects, ieee 2014 dotnet projects, 2014 ieee mtech projects, 2014 ieee btech projects, 2014 ieee be projects, ieee 2014 projects for cse, 2014 ieee cse projects, 2014 ieee it projects, 2014 ieee ece projects, 2014 ieee mca projects, 2014 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2014 mtech projects, 2014 mphil projects, 2014 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2014 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2014 ieee omnet++ projects, ieee 2014 oment++ project, innovative ieee projects, latest ieee projects, 2014 latest ieee projects, ieee cloud computing projects, 2014 ieee cloud computing projects, 2014 ieee networking projects, ieee networking projects, 2014 ieee data mining projects, ieee data mining projects, 2014 ieee network security projects, ieee network security projects, 2014 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2014 wireless networking projects ieee, 2014 ieee web service projects, 2014 ieee soa projects, ieee 2014 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2014 IEEE java projects,2014 ieee Project Titles, 2014 IEEE cse Project Titles, 2014 IEEE NS2 Project Titles, 2014 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2014 - 2014 ... Image Processing. IEEE 2014 - 2014 Projects | IEEE Latest Projects 2014 - 2014 | IEEE ECE Projects2014 - 2014, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2014 IEEE C#, C Sharp Project Titles, 2014 IEEE EmbeddedProject Titles, 2014 IEEE NS2 Project Titles, 2014 IEEE Android Project Titles. 2014 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2014, IEEE 2014 PROJECT TITLES, M.TECH. PROJECTS 2014, IEEE 2014 ME PROJECTS.
The Biological Nervous System and IPv6 Data Communication: Innovation in Knowledge Integration
 
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IPv4 and its route protocol design is based on the traditional post office concept to deliver data packages, as though multiprotocol data switching (PMLS) is familiar with TNT. However, traditional mail can have long latency and plenty of routing resources. Internet data has to be alien but has limited routing resources. This Internet situation will be inherited by an IPv6 network as well, but millions of additional IP addresses. We thus need more advanced reference for IPv6 network data communication. This presentation will base on three levels of knowledge research methodology, which initial reference sample or truth from natural environment to solve technology knowledge. Network science will assist resource-base theory to calculate possible data transmission. I assume the human body is the highest system as we know, with a nervous system responsible for transmitting data among those many millions of cells. I convert the human nerves system statement to the Internet, which can foresee the future structure of network design. This research paper will present three levels of knowledge research methodology, (Para)sympathetic nerve network formulation or intelligent package routing, and cloud computing and structured internet. (By: Shen Zhou: Student, Information Technology, Curtin University) The slides for this presentation may be downloaded at: https://skydrive.live.com/?cid=ef3a191d27780b27&sc=documents#!/?cid=ef3a191d27780b27&permissionsChanged=1&id=EF3A191D27780B27!526

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