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FP tree tabi pptx
 
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Views: 194 taban osman
Cees Taal | Smoothing your data with polynomial fitting: a signal processing perspective
 
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PyData Amsterdam 2017 Github: https://github.com/chtaal/pydata2017 Slides: https://github.com/chtaal/pydata2017/raw/master/ppt/savitzky.pptx The main goal of this talk is to get people acquainted with frequency domain analysis of existing data processing methods, such as polynomial fitting also known as a Savitzky-Golay filter. I will give examples on how to implement these signal processing techniques by using the functionality of the Numpy and Scipy packages. In the field of data processing and analysis we typically have to deal with noisy signals. One possible approach to attenuate the noise is by fitting a polynomial to a subset of samples where the smoothed value is obtained by evaluating the polynomial at the desired time location. In 1964, Abraham Savitzky and Marcel Golay found out that this approach can be interpreted as a convolution between the noisy input signal and a second signal which depends on the settings of the polynomial. Since convolution is a well-known process from the field of signal processing this facilitates frequency domain analysis of such a polynomial smoother. This gives better insights on how to choose free parameters such as the degree of the polynomial and the number of samples used in the fit. The main goal of this talk is to get people acquainted with frequency domain analysis of existing data processing methods, such as polynomial fitting. I will give examples on how to implement these techniques by using the functionality of the Numpy and Scipy packages.
Views: 1863 PyData
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… Visit: https://www.topicsforseminar.com to Download the Big Data PPT
Views: 31189 Topics For Seminar
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
Stanford Webinar: Infrastructure Project Finance
 
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What do you know about infrastructure project finance? Hear Michael Bennon discuss the current state of infrastructure project finance and how it can be utilized in the United States.
Views: 2765 stanfordonline
Construct FP Tree
 
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طريقة بناء ورسم شجرة ترددات الانماط فى قواعد الارتباط فى مجال تنقيب البيانات
Views: 19808 Zuhair abaza
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: 5970 NanoBio Node
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: 196 SecurityTubeCons
Understanding FP Growth algorithm
 
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Understanding FP Growth algorithm. :)
Views: 994 Priyanka Sharma
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: 462 ckleinastro
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: 309540 caltech
15-Association Rule (apriori principle)
 
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کەمپینى بە کوردى کردنى زانست لەزانکۆى گەشەپێدانى مرۆیى
Views: 392 kazheen .O
Data mining FP Growth (Arabic)
 
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Data mining FP-Growth tree construction arabic
Views: 11960 ahmed fawzy
FP Growth | FP Growth Algorithm | FP Growth Algorithm Example | Data Mining
 
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Views: 1231 Learning With Mahamud
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: 127876 MATLAB
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: 2196 sim0nsays
INTRODUCTION TO CYBER CRIME AND ITS PREVENTION IN HINDI
 
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Find the notes of COMPUTER FORENSICS in this link - https://viden.io/knowledge/computer-forensics-ppt?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 94844 LearnEveryone
How to Create Presentation Slides in Jupyter
 
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http://nbviewer.jupyter.org/github/alamgirh/alamgirh.github.io/blob/master/jupyter/Quadratic%20Equations.ipynb
Views: 10738 Alamgir Hossain
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: 7023 Charlie Berger
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: 230 ICPSR
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: 1072 Onur Mutlu Lectures
Decoding the Science of Decision Trees! Learn from Experts | Webinar -1 | Edureka
 
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Watch Sample Class recording: http://goo.gl/OBlNnC This course is designed for professionals who aspire to learn 'R' language for Analytics. The course starts from the very basics like: Introduction to R programming, how to import various formats of Data, manipulate it, etc. to advanced topics like: Data Mining Technique, performing Predictive Analysis to find optimum results based on past data, Data Visualisation using R Commander, Deducer, etc. This video helps you to learn following topics : 1.The Classic Bank Challenge !! 2.The Available Options for Solution 3.Why Decision Tree 4.How Decision Tree Meatholody Works ? Related Posts : http://goo.gl/biYHb7 Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to Selenium have extensively been covered in our course 'Testing With Selenium WebDriver’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 254 edureka!
Human resources, CRM, data mining and social media concept - officer looking for employee represente
 
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To download this template in PowerPoint format (.pptx) please go to link below: http://www.smiletemplates.com/powerpoint-templates/human-resources-crm-data-mining-and-social-media-concept-officer-looking-for-employee-represente/09570/ If you want see some related templates on these theme, please go to: http://www.smiletemplates.com/search/powerpoint-templates/professional/0.html
3-FP Tree Growth
 
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FP stands for frequent pattern. In the first pass, the algorithm counts occurrence of items (attribute-value pairs) in the dataset, and stores them to 'header table'. In the second pass, it builds the FP-tree structure by inserting instances. Items in each instance have to be sorted by descending order of their frequency in the dataset, so that the tree can be processed quickly. Items in each instance that do not meet minimum coverage threshold are discarded. If many instances share most frequent items, FP-tree provides high compression close to tree root.
Views: 7826 Mena A.A
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: 10625 Jeromy Anglim
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: 282 PoweredTemplate.com
[PURDUE MLSS] Classic and Modern Data Clustering by Marina Meilă (Part 1/8)
 
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Lecture slides: http://learning.stat.purdue.edu/mlss/_media/mlss/meila.pdf Abstract of the lecture: Clustering, or finding groups in data, is as old as machine learning itself. However, as more people use clustering in a variety of settings, the last few years we have brought unprecedented developments in this field. This tutorial will survey the most important clustering methods in use today from a unifying perspective, and will then present some of the current paradigms shifts in data clustering. See other lectures at Purdue MLSS Playlist: http://www.youtube.com/playlist?list=PL2A65507F7D725EFB&feature=view_all
Views: 1160 Purdue University
Beauty Industry - Competitive Analytics
 
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Analysis of social media engagement for the top cosmetics brands, based on ShareIQ platform data for 2017.
Views: 108 ShareIQ
An optimized algorithm for association rule mining using FP tree | Final Year Projects 2016
 
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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: 238 ClickMyProject
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: 159 Imi Chitterman
Application of Big Data and Machine Learning in Internet of Things | AI and Big Data in IoT
 
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Know how Big data and Machine learning are shaping the growth of Internet of Things. Learn the introduction to IoT and how it is interconnected with big data, AI and Machine Learning. Know about our analytics programs: PGP-Business Analytics: https://goo.gl/T2Ds4N PGP-Big Data Technology: https://goo.gl/sG8NdL Business Analytics Certificate Program: https://goo.gl/4AFuUg The future of technology lies in data and its analysis. More objects and devices are now connected to the Internet, transmitting the information they gather back for analysis. Three terms that have been discussed in relation to this future: Big Data, AI and The Internet of Things (IoT); It’s hard to talk about one without the other two, and although they are not the same, the practices are closely intertwined. About the Speaker Vijayakeerthi Jayakumar is a Data Scientist at Cognizant. He has 7+ years of industry experience in Analytics & Data Science stream. He has rich experience in building machine learning based solutions to create business impact. Strategic & Performance oriented executive focused on mission and goals with proven track record in handling business requirements & complex problem solving using statistical techniques.
Views: 3062 Great Learning
Clustering Individual Transactional Data for Masses of Users
 
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Author: Riccardo Guidotti, National Research Council (CNR) Abstract: Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 259 KDD2017 video
Understanding and Applying Self-Attention for NLP - Ivan Bilan
 
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PyData Berlin 2018 Understanding attention mechanisms and self-attention, presented in Google's "Attention is all you need" paper, is a beneficial skill for anyone who works on complex NLP problems. In this talk, we will go over the main parts of the Google Transformer self-attention model and the intuition behind it. Then we will look on how this architecture can be used for other NLP tasks, i.e. slot filling. Slides: https://www.dropbox.com/s/hri8veio4rep5g4/Self-Attention_for_NLP_by_Ivan_Bilan.pptx?dl=0 --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1121 PyData
Quantum hacking - Vadim Makarov part 2
 
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This is the last lecture in the series. In this lecture I continue with more attack examples on practical quantum cryptography schemes, and discuss how the research and manufacturing community handles this security problem. If you want an introduction to quantum hacking please start with lecture 2 (http://goo.gl/CpXYpr) of the series. If you want an introduction into quantum cryptography, start with lecture 1 (http://goo.gl/vue6U2) Presentation slides of the entire lecture course can be downloaded at: Power Point (95 MiB, with videos and animations) - http://www.vad1.com/lab/presentations/Makarov-20140801-IQC-short-course.pptx PDF (14.8 MiB, static images only) - http://www.vad1.com/lab/presentations/Makarov-20140801-IQC-short-course.pd Vadim Makarov is a research assistant professor at the Institute for Quantum Computing, heading the Quantum hacking lab - http://www.vad1.com/lab/ This course was part of a lecture series hosted by CryptoWorks21 in August 2014 in Waterloo, Canada Find out more about IQC! Website - https://uwaterloo.ca/institute-for-quantum-computing/ Facebook - https://www.facebook.com/QuantumIQC Twitter - https://twitter.com/QuantumIQC
Clustering Individual Transactional Data for Masses of Users
 
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Clustering Individual Transactional Data for Masses of Users Riccardo Guidotti (University of Pisa) Anna Monreale (University of Pisa) Mirco Nanni (KDD-Lab ISTI-CNR Pisa) Fosca Giannotti (ISTI-CNR) Dino Pedreschi (University of Pisa) Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. More on http://www.kdd.org/kdd2017/
Views: 4698 KDD2017 video
How To Work With Text in PowerPoint in Hindi
 
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Learn how to work with Text in MS PowerpPoint slide. You will learb how to Add Text in PowerPoint Edit Text in PowerPoint Delete Text in PowerPoint Format Text in PowerPoint To learn more about how to use PowerPoint please visit http://www.myelesson.org/ 10 Most Used Formulas MS Excel https://www.youtube.com/watch?v=KyMj8HEBNAk Learn Basic Excel Skills For Beginners || Part 1 https://www.youtube.com/watch?v=3kNEv3s8TuA 10 Most Used Excel Formula https://www.youtube.com/watch?v=2t3FDi98GBk **Most Imporant Excel Formuls Tutorials** Learn Vlookup Formula For Beginners in Excel https://www.youtube.com/watch?v=vomClevScJQ 5 Excel Questions Asked in Job Interviews https://www.youtube.com/watch?v=7Iwx4AMdij8 Create Speedometer Chart In Excel https://www.youtube.com/watch?v=f6c93-fQlCs Learn the Basic of Excel for Beginners || Part 2 https://www.youtube.com/watch?v=qeMSV9T1PoI Create Pareto Chart In Excel https://www.youtube.com/watch?v=2UdajrDMjRE How to Create Dashboard in Excel https://www.youtube.com/watch?v=RM8T1eYBjQY Excel Interview Questions & Answers https://www.youtube.com/watch?v=Zjv1If63nGU
Views: 52646 My E-Lesson
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: 24820 Captain Corrosion
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: 1183 Anand Maurya
XRF Bootcamp: ArTax Software
 
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Instructions on how to export the data from the S1PXRF software and to utilize the Bruker ArTax software for post-processing XRF Boot Camp for Conservators is a series of focused workshops on the fundamentals of X-ray fluorescence spectroscopy and data interpretation, developed and carried out by the Getty Conservation Institute in partnership with the Institute for the Preservation of Cultural Heritage at Yale University.
IEEE 2014 VLSI ULTRA HIGH THROUGHPUT LOW POWER pptx
 
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17-frequent pattern part2
 
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Description کەمپینى بە کوردى کردنى زانست لە زانکۆى گەشەپێدانى مرۆیى
Views: 178 chopi
10/12/16 Data Science with Kaggle Decal Lecture 11: Validation and Model Review
 
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The beginning of the lecture cut off. It was focused on the lecture slides, however, which are available for download here: https://github.com/kaggledecal/kaggle_fa16/raw/master/slides/day11.pptx Repo: https://github.com/kaggledecal/kaggle_fa16 Before working, make sure you run `git pull` in your local copy of the kaggle_16 repo! If you haven't clone the repo yet, run this in terminal: `git clone https://github.com/kaggledecal/kaggle_fa16.git`
Views: 102 Phillip Kuznetsov
Example FP Growth
 
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Views: 1065 Akaner4
Free downloads tutorials for all programs| How to|Procedure|Sachem kenya|Tanzania
 
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F P Tree
 
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Views: 61 LeakyMeat
The Art of Clustering -- The Biotech Way
 
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Dr. Juergen Krause of the UPEI School of Business delivers his Research Breakfast lecture: The Art of Clustering -- The Biotech Way.
Yelp Dataset Challenge: An Analysis of Business Success Based on Location Clustering
 
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Yelp Dataset Challenge. This system can cluster all restaurants based on geo-location, create a metric to calculate the average success value for each area, and make predictions on the future business success. From Brigitte Harder and Zhidong Wu.
Views: 407 Zhidong Wu
Onur Mutlu at Bogazici University, "Memory QoS" Lecture 2.3 (part 2)
 
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Date: June 17, 2013 Slides (pptx): https://docs.google.com/file/d/0BxuWyjxYMQGuSi1LMjFLZHZDTjQ/edit?usp=sharing Prof. Onur Mutlu lectures in Bogazici University Lecture 1: Multi-core Architectures and Shared Resource Management: Fundamentals and Recent Research, 6-7-10 June 2013 Lecture 2: Memory Systems in the Multi-Core Era, 13-14-17 June 2013
Views: 94 boun lectures