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Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 137135 SciShow
Ben Goertzel - AGI to Cure Aging
 
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Full Video: https://www.youtube.com/watch?v=uvEjR1DWY5Q Dr. Ben Goertzel is widely recognized as the father of Artificial General Intelligence. In this talk he discusses: AI, artificial intelligence, artificial general intelligence, deep learning, life extension, longevity, robotics, humanoid, transhumanism. Ben Goertzel: https://en.wikipedia.org/wiki/Ben_Goertzel Ben Goertzel is Chief Scientist of financial prediction firm Aidyia Holdings; Chairman of AI software company Novamente LLC, which is a privately held software company, and bioinformatics company Biomind LLC, which is a company that provides advanced AI for bioinformatic data analysis (especially microarray and SNP data); Chairman of the Artificial General Intelligence Society and the OpenCog Foundation; Vice Chairman of futurist nonprofit Humanity+; Scientific Advisor of biopharma firm Genescient Corp.; Advisor to the Singularity University; Research Professor in the Fujian Key Lab for Brain-Like Intelligent Systems at Xiamen University, China; and general Chair of the Artificial General Intelligence conference series, an American author and researcher in the field of artificial intelligence. He is an advisor to the Machine Intelligence Research Institute (formerly the Singularity Institute) and formerly its Director of Research. His research work encompasses artificial general intelligence, natural language processing, cognitive science, data mining, machine learning, computational finance, bioinformatics, virtual worlds and gaming and other areas. He has published a dozen scientific books, 100+ technical papers, and numerous journalistic articles. He actively promotes the OpenCog project that he co-founded, which aims to build an open source artificial general intelligence engine. He is focused on creating benevolent superhuman artificial general intelligence; and applying AI to areas like financial prediction, bioinformatics, robotics and gaming. --------- Facebook: https://www.facebook.com/agingreversed Tumblr: http://agingreversed.tumblr.com Twitter: https://twitter.com/Aging_Reversed Donate: https://goo.gl/ciSpg1
Views: 13036 Aging Reversed
Knowledge-based Information Retrieval with Wikipedia.
 
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Google Tech Talks October 31, 2008 ABSTRACT In knowledge-based information retrieval, search engines consult external sources of knowledge ontologies, taxonomies, thesauri, glossaries, gazeteers to help process the documents they encounter and the requests they receive. The idea is old, obvious, and compelling but results have been singularly unimpressive. The best performing and most widely used search systems are still those that deal in lexical character patterns without using any structured knowledge to understand them. Wikipedia is changing all that. This open, constantly evolving encyclopedia represents a vast pool of topics and semantic relations. It is arguably the largest knowledge base humanity has ever seen. At last we have a resource that is (or may be) sufficiently broad, deep, and timely to be applicable to open-domain information retrieval. However, it brings its own challenges. Wikipedia's haphazard and only partially machine-readable structure bears little resemblance to the carefully crafted knowledge bases that have been used to assist information retrieval in the past. This talk will discuss Wikipedia's promises and shortcomings, and describe ongoing investigations of how best to apply it to organizing and retrieving information. Speaker: David Milne David Milne is a PhD student at the University of Waikato in New Zealand, where he studies under the supervision of Prof. Ian H. Witten.
Views: 13743 GoogleTechTalks
Forecasting Time Series Data in R | Facebook's Prophet Package 2017 & Tom Brady's Wikipedia data
 
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An example of using Facebook's recently released open source package prophet including, - data scraped from Tom Brady's Wikipedia page - getting Wikipedia trend data - time series plot - handling missing data and log transform - forecasting with Facebook's prophet - prediction - plot of actual versus forecast data - breaking and plotting forecast into trend, weekly seasonality & yearly seasonality components prophet procedure is an additive regression model with following components: - a piecewise linear or logistic growth curve trend - a yearly seasonal component modeled using Fourier series - a weekly seasonal component forecasting is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 17811 Bharatendra Rai
What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning
 
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What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning - PREDICTIVE ANALYTICS definition - PREDICTIVE ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions." In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization. Furthermore, the converted data can be used for closed-loop product life cycle improvement which is the vision of the Industrial Internet Consortium.
Views: 1022 The Audiopedia
What is DATA STREAM MINING? What does DATA STREAM MINING mean? DATA STREAM MINING meaning
 
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What is DATA STREAM MINING? What does V mean? DATA STREAM MINING meaning - DATA STREAM MINING definition - DATA STREAM MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Views: 580 The Audiopedia
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
 
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 1843 TEDx Talks
SpiegelMining – Reverse Engineering von Spiegel-Online (33c3)
 
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Wer denkt, Vorratsdatenspeicherungen und „Big Data“ sind harmlos, der kriegt hier eine Demo an Spiegel-Online. Seit Mitte 2014 hat David fast 100.000 Artikel von Spiegel-Online systematisch gespeichert. Diese Datenmasse wird er in einem bunten Vortrag vorstellen und erforschen. David Kriesel
Views: 502851 media.ccc.de
The Big Data Setup of the Human Brain Project (ft. Anastasia Ailamaki)
 
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Professor Anastasia Ailamaki discusses the Big Data management challenges of the Human Brain Project. She is full professor of the IC School at EPFL. https://people.epfl.ch/anastasia.ailamaki The Human Brain Project - Video Overview | HumanBrainProject https://www.youtube.com/watch?v=JqMpGrM5ECo Azevedo, Frederico A.C. and Carvalho, Ludmila R.B. and Grinberg, Lea T. and Farfel, José Marcelo and Ferretti, Renata E.L. and Leite, Renata E.P. and Filho, Wilson Jacob and Lent, Roberto and Herculano-Houzel, Suzana (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain, in The Journal of Comparative Neurology. http://onlinelibrary.wiley.com/doi/10.1002/cne.21974/full David A. Drachman (2005). Do we have brain to spare? In Neurology. http://www.neurology.org/content/64/12/2004 https://en.wikipedia.org/wiki/Neuron#Neurons_in_the_brain
Views: 891 ZettaBytes, EPFL
Don't Waste $1000 on Data Recovery
 
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Thanks to DeepSpar for sponsoring this video! Check out their RapidSpar Data Recovery Tool at http://geni.us/rapidspar RapidSpar is the first cloud-driven device built to help IT generalists and other non-specialized users recover client data from damaged or failing HDDs/SSDs Buy HDDs on Amazon: http://geni.us/sLlhDf Buy HDDs on Newegg: http://geni.us/a196 Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/Linustechtips Linus Tech Tips posters at http://crowdmade.com/linustechtips Our Test Benches on Amazon: https://www.amazon.com/shop/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxm... iTunes Download Link: https://itunes.apple.com/us/album/sup... Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnir... Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 1385426 Linus Tech Tips
Handling Class Imbalance Problem in R: Improving Predictive Model Performance
 
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Provides steps for carrying handling class imbalance problem when developing classification and prediction models Download R file: https://goo.gl/ns7zNm data: https://goo.gl/d5JFtq Includes, - What is Class Imbalance Problem? - Data partitioning - Data for developing prediction model - Developing prediction model - Predictive model evaluation - Confusion matrix, - Accuracy, sensitivity, and specificity - Oversampling, undersampling, synthetic sampling using random over sampling examples predictive models are important machine learning and statistical tools related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 10378 Bharatendra Rai
003 - Binary Distance
 
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Get a little "bit" of binary in your life. Learn about binary and binary operators in this video. Some additional "light" reading: http://stackoverflow.com/a/12946226 http://stackoverflow.com/questions/867393/how-do-languages-such-as-python-overcome-cs-integral-data-limits/870429#870429 https://en.wikipedia.org/wiki/Hamming_weight#Efficient_implementation https://gcc.gnu.org/onlinedocs/gcc/Other-Builtins.html https://graphics.stanford.edu/~seander/bithacks.html https://svn.python.org/projects/python/trunk/Objects/longobject.c See you next time! Music: Antonio Vivaldi - Concerto in C major Op.8 No.12, European Archive, https://musopen.org/music/3087/antonio-vivaldi/concerto-in-c-major-op8-no12/ Some images: http://www.snappygoat.com
Views: 309 CoderSnacks
Geoff Webb - Analysis and Mining Large Data Sets
 
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Geoffrey I. Webb is Professor of Computer Science at Monash University, Founder and Director of Data Mining software development and consultancy company G. I. Webb and Associates, and Editor-in-Chief of the journal Data Mining and Knowledge Discovery. Before joining Monash University he was on the faculty at Griffith University from 1986 to 1988 and then at Deakin University from 1988 to 2002. Webb has published more than 180 scientific papers in the fields of machine learning, data science, data mining, data analytics, big data and user modeling. He is an editor of the Encyclopedia of Machine Learning. Webb created the Averaged One-Dependence Estimators machine learning algorithm and its generalization Averaged N-Dependence Estimators and has worked extensively on statistically sound association rule learning. Webb's awards include IEEE Fellow, the IEEE International Conference on Data Mining Outstanding Service Award, an Australian Research Council Outstanding Researcher Award and multiple Australian Research Council Discovery Grants. Webb is a Foundation Member of the Editorial Advisory Board of the journal Statistical Analysis and Data Mining, Wiley Inter Science. He has served on the Editorial Boards of the journals Machine Learning, ACM Transactions on Knowledge Discovery in Data,User Modeling and User Adapted Interaction,and Knowledge and Information Systems. https://en.wikipedia.org/wiki/Geoff_Webb http://www.infotech.monash.edu.au/research/profiles/profile.html?sid=4540&pid=122 http://www.csse.monash.edu.au/~webb Interviewed by Kevin Korb and Adam Ford Many thanks for watching! - Support me via Patreon: https://www.patreon.com/scifuture - Please Subscribe to this Channel: http://youtube.com/subscription_center?add_user=TheRationalFuture - Science, Technology & the Future website: http://scifuture.org
Google's Deep Mind Explained! - Self Learning A.I.
 
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Subscribe here: https://goo.gl/9FS8uF Become a Patreon!: https://www.patreon.com/ColdFusion_TV Visual animal AI: https://www.youtube.com/watch?v=DgPaCWJL7XI Hi, welcome to ColdFusion (formally known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: Why AlphaGo is NOT an "Expert System": https://googleblog.blogspot.com.au/2016/01/alphago-machine-learning-game-go.html “Inside DeepMind” Nature video: https://www.youtube.com/watch?v=xN1d3qHMIEQ “AlphaGo and the future of Artificial Intelligence” BBC Newsnight: https://www.youtube.com/watch?v=53YLZBSS0cc http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html http://www.ft.com/cms/s/2/063c1176-d29a-11e5-969e-9d801cf5e15b.html http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html#tables https://www.technologyreview.com/s/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1 https://www.deepmind.com/ www.forbes.com/sites/privacynotice/2014/02/03/inside-googles-mysterious-ethics-board/#5dc388ee4674 https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1#.4yt5o1e59 http://www.theverge.com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai https://en.wikipedia.org/wiki/Demis_Hassabis https://en.wikipedia.org/wiki/Google_DeepMind //Soundtrack// Disclosure - You & Me (Ft. Eliza Doolittle) (Bicep Remix) Stumbleine - Glacier Sundra - Drifting in the Sea of Dreams (Chapter 2) Dakent - Noon (Mindthings Rework) Hnrk - fjarlæg Dr Meaker - Don't Think It's Love (Real Connoisseur Remix) Sweetheart of Kairi - Last Summer Song (ft. CoMa) Hiatus - Nimbus KOAN Sound & Asa - This Time Around (feat. Koo) Burn Water - Hide » Google + | http://www.google.com/+coldfustion » Facebook | https://www.facebook.com/ColdFusionTV » My music | t.guarva.com.au/BurnWater http://burnwater.bandcamp.com or » http://www.soundcloud.com/burnwater » https://www.patreon.com/ColdFusion_TV » Collection of music used in videos: https://www.youtube.com/watch?v=YOrJJKW31OA Producer: Dagogo Altraide Editing website: www.cfnstudios.com Coldfusion Android Launcher: https://play.google.com/store/apps/details?id=nqr.coldfustion.com&hl=en » Twitter | @ColdFusion_TV
Views: 2917298 ColdFusion
Wikimania 2011 - 2nd Day: Statistical analysis; MediaWiki development
 
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Room: Gilboa Date: 5.8.2011 00:07 - How to handle data and run statistical analyses in MediaWiki Juha Villman Presentation will contain brief introduction to Opasnet and the ideas behind it. Main focus will be on data related issues: how to handle and how to use massive amounts of data in MediaWiki and as well demonstrate how easy it is to run R on wiki. http://wikimania2011.wikimedia.org/wiki/Submissions/How_to_handle_data_and_run_statistical_analyses_in_Mediawiki 15:40 - ResourceLoader Roan Kattouw, Trevor Parscal MediaWiki 1.17 was deployed to all Wikimedia wikis in February, with ResourceLoader as its flagship feature. ResourceLoader is a JavaScript and CSS delivery system that modernizes the development and delivery of client-side resources. http://wikimania2011.wikimedia.org/wiki/Submissions/ResourceLoader 38:20 - Opening up Wikipedia's data: A lightweight approach to Wikipedia as a platform Dario Taraborelli, Diederik van Liere, Ryan Lane There is a final frontier where Wikipedia and its sister projects are not as open as they could or should be: non-human interactions. Wikimedia's infrastructure is designed to make it possible for millions of humans worldwide to freely reuse its contents, but it falls short of providing tools to allow third-party services to easily reuse its data. The goal of this presentation is to start a discussion on what it takes to rethink Wikipedia as a platform (WAAP) to facilitate the reuse of its contents in the form of structured data. In this talk we will focus on two technologies—Wikilytics and OAuth—that, combined, could spearhead the creation of an ecosystem of new services based on Wikipedia's data. Wikilytics is an analytics platform to answer questions about the different Wikipedia communities. http://wikimania2011.wikimedia.org/wiki/Submissions/Opening_up_Wikipedia%27s_data:_A_lightweight_approach_to_Wikipedia_as_a_platform 53:45 - A Qt library for MediaWiki, and what you can do with it Guillaume Paumier This short talk will present a Qt library to interact with the MediaWiki API, developed during a university project. Examples of applications based on the library will be demoed, including basic editing and uploading programs, plasma desktop widgets, and a KIPI export plug-in to mass upload photos from desktop applications. http://wikimania2011.wikimedia.org/wiki/Submissions/A_Qt_library_for_MediaWiki,_and_what_you_can_do_with_it
Views: 771 Wikimedia Israel
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 135600 Siraj Raval
What does data mining mean?
 
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What does data mining mean? A spoken definition of data mining. Intro Sound: Typewriter - Tamskp Licensed under CC:BA 3.0 Outro Music: Groove Groove - Kevin MacLeod (incompetech.com) Licensed under CC:BA 3.0 Intro/Outro Photo: The best days are not planned - Marcus Hansson Licensed under CC-BY-2.0 Book Image: Open Book template PSD - DougitDesign Licensed under CC:BA 3.0 Text derived from: http://en.wiktionary.org/wiki/data_mining Text to Speech powered by TTS-API.COM
What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning & explanation
 
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What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning - DATA WAREHOUSE definition - DATA WAREHOUSE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place and are used for creating analytical reports for knowledge workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting. The typical Extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. The main source of the data is cleansed, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. Mere congregation of data to single database so a single query engine can be used to present data is an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. Add value to operational business applications, notably customer relationship management (CRM) systems. Make decision–support queries easier to write. Optimized data warehouse architectures allow data scientists to organize and disambiguate repetitive data. The environment for data warehouses and marts includes the following: Source systems that provide data to the warehouse or mart; Data integration technology and processes that are needed to prepare the data for use; Different architectures for storing data in an organization's data warehouse or data marts; Different tools and applications for the variety of users; Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes. In regards to source systems listed above, Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases"....
Views: 1015 The Audiopedia
Why Does Linus Pirate Windows??
 
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Enter Soundcore's giveaway for a chance to win 1 of 300 speakers here: http://geni.us/wI9RL1 Customize your own Backplate for your GPU or SSD with CableMod: http://geni.us/kw6D1V Activate Windows. A surprisingly frequent appearance on Linus Tech Tips. But why would we pirate Windows? Will Microsoft sue us? Discuss on the forum: https://linustechtips.com/main/topic/967694-why-does-linus-pirate-windows/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/... Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/Li... Linus Tech Tips posters at http://crowdmade.com/linustechtips Our Test Benches on Amazon: https://www.amazon.com/shop/linustech... Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxm... iTunes Download Link: https://itunes.apple.com/us/album/sup... Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnir... Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 3577023 Linus Tech Tips
What is KNOWLEDGE DISCOVERY? What does KNOWLEDGE DISCOVERY mean? KNOWLEDGE DISCOVERY meaning
 
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What is KNOWLEDGE DISCOVERY? What does KNOWLEDGE DISCOVERY mean? KNOWLEDGE DISCOVERY meaning - KNOWLEDGE DISCOVERY definition - KNOWLEDGE DISCOVERY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. nowledge discovery describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. It is often described as deriving knowledge from the input data. Knowledge discovery developed out of the data mining domain, and is closely related to it both in terms of methodology and terminology. The most well-known branch of data mining is knowledge discovery, also known as knowledge discovery in databases (KDD). Just as many other forms of knowledge discovery it creates abstractions of the input data. The knowledge obtained through the process may become additional data that can be used for further usage and discovery. Often the outcomes from knowledge discovery are not actionable, actionable knowledge discovery, also known as domain driven data mining, aims to discover and deliver actionable knowledge and insights. Another promising application of knowledge discovery is in the area of software modernization, weakness discovery and compliance which involves understanding existing software artifacts. This process is related to a concept of reverse engineering. Usually the knowledge obtained from existing software is presented in the form of models to which specific queries can be made when necessary. An entity relationship is a frequent format of representing knowledge obtained from existing software. Object Management Group (OMG) developed specification Knowledge Discovery Metamodel (KDM) which defines an ontology for the software assets and their relationships for the purpose of performing knowledge discovery of existing code. Knowledge discovery from existing software systems, also known as software mining is closely related to data mining, since existing software artifacts contain enormous value for risk management and business value, key for the evaluation and evolution of software systems. Instead of mining individual data sets, software mining focuses on metadata, such as process flows (e.g. data flows, control flows, & call maps), architecture, database schemas, and business rules/terms/process.
Views: 1718 The Audiopedia
Facebook's Cambridge Analytica data scandal, explained
 
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Cambridge Analytica improperly obtained data from as many as 50 million people. That's put Mark Zuckerberg on the defensive. The Verge's Silicon Valley editor Casey Newton reports. Subscribe: https://goo.gl/G5RXGs Check out our full video catalog: https://goo.gl/lfcGfq Visit our playlists: https://goo.gl/94XbKx Like The Verge on Facebook: https://goo.gl/2P1aGc Follow on Twitter: https://goo.gl/XTWX61 Follow on Instagram: https://goo.gl/7ZeLvX Read More: http://www.theverge.com
Views: 591755 The Verge
Duke Pesta on Common Core – Six Years Later
 
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On March 2, 2016 Dr. Duke Pesta spoke on the dangers of Common Core and discussed the new federal law “Every Student Succeeds Act” (ESSA). No doubt one of the most outspoken critic of the Common Core State Standards, Dr. Pesta exposes the origins and dangers of the Common Core scheme. Through his and other's efforts, people are beginning to fight back on behalf of their children. From this video, you will learn about the government's overreach, its concealment and outright lies that drive Common Core. Throughout his presentation, Dr. Pesta discusses the current state of the fight, identifies new threats and updates us on the current condition of public education in America. With over 450 talks in 40 states behind him, he offers his perspective on the best ways to push back against this ploy to manipulate the heart and minds of our children. Speaker Bio: Dr. Duke Pesta Freedom Project Education Academic Director Dr. Duke Pesta received his M.A. in Renaissance Literature from John Carroll University and his Ph.D. in Shakespeare and Renaissance Literature from Purdue University. He has taught at major research institutions and small liberal arts colleges and currently is a professor of English at the University of Wisconsin, Oshkosh and the Academic Director of Freedom Project Education. This event was held at the Yorba Linda First Baptist Church in Yorba Linda, CA. and was sponsored by the Faithful Christian Servants, Adelphia Classical Christian Academy and Reclaim Public Education. For more information: Website: http://www.FaithfulChristianServants.com To learn more about Dr. Pesta's and the Freedom Project Education: Website: http://www.FPEusa.org Want to do something for your children? Complete an Opt Out Form: http://www.pacificjustice.org/california-common-core-data-opt-out-form.html For free legal advice on Common Core, contact Brad Dacus, Esq. Website: http://www.pacificJustice.org
Views: 44839 Costa Mesa Brief
What Is The Database Processing?
 
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Data is huge in size making it suboptimal to move data from corporate sans processing servers. Googleusercontent searchin database processing, sometimes referred to as in analytics, refers the integration of data analytics into warehousing functionality processing. In database processing and in memory analytics with teradata sas support. A relational database is a set of data stored in one or more tables computer. In most cases data processing and management are critical components of business organizationsdata refers to the process performing specific operations on a set or database. Later, as microcomputers gained popularity, database technology migrated to micros and was used for single user, personal applications. Active in database processing to support ambient assisted living. Database processing wikipedia in database wikipedia en. However, with the new separation of components in version 4. A database is an organized collection of facts and information, such as records on employees, inventory, in processing lets marketers anticipate behaviors, engage customers, reward loyalty retain loyal customers longer. Instead of moving the data, processing to data is principle advocated by in database processing, while memory distributed relational. Connect to all data where insights may reside regardless of location or size database processing was originally used in major corporations and large organizations as the basis transaction systems. The administrator's challenge is to selectively deploy this technology fully use its multiprocessing 12 aug 2014 as an alternative the existing software architectures that underpin development of smart homes and ambient assisted living (aal) systems, work presents a database centric architecture takes advantage active databases in processingDatabase processing wikipediadatabase processingthe history. This chapter introduces parallel processing and database technologies, which offer great advantages for online transaction decision support applications. Introduction to sas in database processing sas(r) 9. What is in database processing? Researchgate. Proc report in database processing for proc. Each row contains a sequence of values, one for each column sas in database processing with teradatacustomer requirements to understand and act on critical business issues such as fraud detection, credit risk, price optimization, warranty analysis, customer retention demand efficient handling utilization intelligence. Parallel processing & parallel databases oracle help center. A new era of high performance analytics has emerged. What is in database processing? (with picture) wisegeek. The most common application of dock is to process a database molecules find potential inhibitors or ligands target macromolecule. Distributed processing and distributed databases myreadingroom. Replace sql coding for large scale data blending and analytic processes within databases. Bi), data integration, and sas has announced a roadmap for
Bitcoin: How Cryptocurrencies Work
 
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Whether or not it's worth investing in, the math behind Bitcoin is an elegant solution to some complex problems. Hosted by: Michael Aranda Special Thanks: Dalton Hubble Learn more about Cryptography: https://www.youtube.com/watch?v=-yFZGF8FHSg ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters—we couldn't make SciShow without them! Shout out to Bella Nash, Kevin Bealer, Mark Terrio-Cameron, Patrick Merrithew, Charles Southerland, Fatima Iqbal, Benny, Kyle Anderson, Tim Curwick, Will and Sonja Marple, Philippe von Bergen, Bryce Daifuku, Chris Peters, Patrick D. Ashmore, Charles George, Bader AlGhamdi ---------- Like SciShow? Want to help support us, and also get things to put on your walls, cover your torso and hold your liquids? Check out our awesome products over at DFTBA Records: http://dftba.com/scishow ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://bitinfocharts.com/ https://chrispacia.wordpress.com/2013/09/02/bitcoin-mining-explained-like-youre-five-part-2-mechanics/ https://www.youtube.com/watch?v=Lx9zgZCMqXE https://www.youtube.com/watch?v=nQZUi24TrdI https://bitcoin.org/en/how-it-works http://www.forbes.com/sites/investopedia/2013/08/01/how-bitcoin-works/#36bd8b2d25ee http://www.makeuseof.com/tag/how-does-bitcoin-work/ https://blockchain.info/charts/total-bitcoins https://en.bitcoin.it/wiki/Controlled_supply https://www.bitcoinmining.com/ http://bitamplify.com/mobile/?a=news Image Sources: https://commons.wikimedia.org/wiki/File:Cryptocurrency_Mining_Farm.jpg
Views: 2563968 SciShow
What is SCIENTOMETRICS? What does SCIENTOMETRICS mean? SCIENTOMETRICS meaning & explanation
 
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What is SCIENTOMETRICS? What does SCIENTOMETRICS mean? SCIENTOMETRICS meaning - SCIENTOMETRICS definition - SCIENTOMETRICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Scientometrics is the study of measuring and analysing science, technology and innovation. Major research issues include the measurement of impact, reference sets of articles to investigate the impact of journals and institutes, understanding of scientific citations, mapping scientific fields and the production of indicators for use in policy and management contexts. In practice there is a significant overlap between scientometrics and other scientific fields such as bibliometrics, information systems, information science and science of science policy. Modern scientometrics is mostly based on the work of Derek J. de Solla Price and Eugene Garfield. The latter created the Science Citation Index and founded the Institute for Scientific Information which is heavily used for scientometric analysis. A dedicated academic journal, Scientometrics, was established in 1978. The industrialization of science increased the quantity of publications and research outcomes and the rise of the computers allowed effective analysis of this data. While the sociology of science focused on the behavior of scientists, scientometrics focused on the analysis of publications. Accordingly, scientometrics is also referred to as the scientific and empirical study of science and its outcomes. Later, around the turn of the century, evaluation and ranking of scientists and institutions came more into the spotlights. Based on bibliometric analysis of scientific publications and citations, the Academic Ranking of World Universities ("Shanghai ranking") was first published in 2004 by the Shanghai Jiao Tong University. Impact factors became an important tool to choose between different journals and the rankings such as the Academic Ranking of World Universities and the Times Higher Education World University Rankings (THE-ranking) became a leading indicator for the status of universities. The h-index became an important indicator of the productivity and impact of the work of a scientist. However, alternative author-level indicators has been proposed (see for example). Around the same time, interest of governments in evaluating research for the purpose of assessing the impact of science funding increased. As the investments in scientific research were included as part of the U.S. American Recovery and Reinvestment Act of 2009 (ARRA), a major economic stimulus package, programs like STAR METRICS were set up to assess if the positive impact on the economy would actually occur. Methods of research include qualitative, quantitative and computational approaches. The main foci of studies have been on institutional productivity comparisons, institutional research rankings, journal rankings establishing faculty productivity and tenure standards, assessing the influence of top scholarly articles, and developing profiles of top authors and institutions in terms of research performance One significant finding in the field is a principle of cost escalation to the effect that achieving further findings at a given level of importance grow exponentially more costly in the expenditure of effort and resources. However, new algorithmic methods in search, machine learning and data mining are showing that is not the case for many information retrieval and extraction-based problems. Related fields are the history of science and technology, philosophy of science and sociology of scientific knowledge. Journals in the field include Scientometrics, Journal of the American Society for Information Science and Technology, and Journal of Informetrics. The International Society for Scientometrics and Informetrics founded in 1993 is an association of professionals in the field.
Views: 176 The Audiopedia
Social Network Analysis The Basics
 
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Explains the basic social network analysis vocabulary with a suggested reading list. Defines the Node and Link Condor MySQL tables for email, web, wikipedia, twitter, facebook and video databases. Duration: 15mins0secs. Links within video: http://moreno.ss.uci.edu/ http://moreno.ss.uci.edu/pubs.html http://www.amazon.com/dp/1594577145/ref=rdr_ext_tmb http://en.wikipedia.org/wiki/Centrality#Degree_centrality http://en.wikipedia.org/wiki/Dense_graph http://en.wikipedia.org/wiki/Bridge_(graph_theory) http://en.wikipedia.org/wiki/Social_network http://en.wikipedia.org/wiki/Betweenness#Betweenness_centrality http://www.insna.org/ http://coinsconference.org/ http://savannah09.coinsconference.org/ http://savannah10.coinsconference.org/ http://basel11.coinsconference.org http://galaxyadvisors.com/index.php Condor video page at: http://www.galaxyadvisors.com/science-of-swarms/condor-videos.html
Views: 14507 Ken Riopelle
What is DATA VISUALIZATION? What does DATA VISUALIZATION mean? DATA VISUALIZATION meaning
 
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What is DATA VISUALIZATION? What does DATA VISUALIZATION mean? DATA VISUALIZATION meaning - DATA VISUALIZATION definition - DATA VISUALIZATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information". A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables. Data visualization is both an art and a science. It is viewed as a branch of descriptive statistics by some, but also as a grounded theory development tool by others. The rate at which data is generated has increased. Data created by internet activity and an expanding number of sensors in the environment, such as satellites, are referred to as "Big Data". Processing, analyzing and communicating this data present a variety of ethical and analytical challenges for data visualization. The field of data science and practitioners called data scientists have emerged to help address this challenge. Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information". Indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention. Not limited to the communication of an information, a well-crafted data visualization is also a way to a better understanding of the data (in a data-driven research perspective), as it helps uncover trends, realize insights, explore sources, and tell stories. Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization.
Views: 2401 The Audiopedia
Snorkel: Dark Data and Machine Learning -  Christopher Ré
 
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Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other "dark" data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We'll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark.
Views: 2645 Databricks
How to Build a Rocket Engine in Your Kitchen (Experiment Episode)
 
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Hank demonstrates how to build a hybrid rocket engine in your kitchen! Hosted by: Hank Green Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://gizmodo.com/how-to-turn-dry-pasta-into-a-rocket-engine-1514944607 https://web.stanford.edu/~cantwell/AA283_Course_Material/AA283_Course_Notes/AA283_Aircraft_and_Rocket_Propulsion_Ch_11_BJ_Cantwell.pdf http://www.qrg.northwestern.edu/projects/vss/docs/propulsion/2-what-are-the-types-of-rocket-propulsion.html https://www.grc.nasa.gov/www/k-12/airplane/lrockth.html https://www.sciencedirect.com/science/article/pii/S1270963806001404 https://phys.org/news/2015-07-student-hasten-dawn-hybrid-rocket.html https://www.sciencedirect.com/science/article/pii/S0094576516301941 https://www.acs.org/content/dam/acsorg/education/resources/k-8/science-activities/chemicalphysicalchange/chemicalreactions/heat-up-to-some-cool-reactions.pdf http://www.qrg.northwestern.edu/projects/vss/docs/propulsion/2-what-is-an-oxidizer.html https://www.scientificamerican.com/article/exploring-enzymes/ https://www.space.com/330-spaceshipone-rocket-engine-upgrade.html http://www.scielo.br/pdf/jbsmse/v32n4/v32n4a12.pdf Media: https://www.istockphoto.com/my/photo/science-fair-project-gm460811091-20917190 https://commons.wikimedia.org/wiki/File:Two_Space_Shuttle_SRBs_on_the_Crawler_transporter.jpg https://commons.wikimedia.org/wiki/File:Space_Shuttle_Columbia_launching.jpg https://commons.wikimedia.org/wiki/File:LiquidFuelRocketSchematic.jpg https://commons.wikimedia.org/wiki/File:Sojuz_TMA-9_into_flight.jpg
Views: 134763 SciShow
European Union Targets YouTube With Internet Censorship Legislation Coming This January 2019
 
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My most recent video "Safest Cities USA: Thousand Oaks, CA (From Wild West Bar Shootouts to Wildfires) We Have it all Covered!" https://www.youtube.com/watch?v=rWGa4ksHaG8 --~-- It quietly went through the European Union Parliament on September 12th, 2018. This is how Wikipedia describes it. "The Directive on Copyright in the Digital Single Market 2016/0280(COD), also known as the EU Copyright Directive, is a proposed European Union directive intended to harmonise aspects of the European Union copyright law and moved towards a Digital Single Market. First introduced by the European Parliament Committee on Legal Affairs on 20 June 2018, the directive currently has been approved by the European Parliament on 12 September 2018, and will enter formal Trilogue discussions that are expected to conclude in January 2019. If formalised, each of the EU's member countries would then be required to enact laws to support the directive. The European Council describe their key goals as protecting press publications, reducing the "value gap" between the profits made by internet platforms and content creators, encouraging "collaboration" between these two groups, and creating copyright exceptions for text and data mining. The directive's specific proposals include giving press publishers direct copyright over use of their publications by internet platforms such as online news aggregators (Article 11) and requiring websites who primarily host content posted by users to take "effective and proportionate" measures to prevent unauthorized postings of copyrighted content or be liable for their users' actions (Article 13)." We have already seen how censorship can control a social media platform such as YouTube and Facebook. If we are to trust on-line A.I. enhanced algorithms to screen what we - as a group of individuals - can and cannot post on our social media platforms without risking getting hit and subsequently censored by Google or other search engines then this is the beginning of the end of Free Speech on the Internet. If the European Union can get away with imposing Draconian Internet Censorship Laws then we as a Human Race may well be whipped and beating into oblivion. That's my opinion. Who is going to take that away from me? #EuropeanUnionArt13 #YouTubePurge2 #InternetCensorship Music credit: Light Awash by Kevin MacLeod is licensed under a Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100175 Artist: http://incompetech.com/ Related Articles: https://www.theverge.com/2018/9/13/17854158/eu-copyright-directive-article-13-11-internet-censorship-google https://en.wikipedia.org/wiki/Directive_on_Copyright_in_the_Digital_Single_Market Related videos: UNIRock2 YouTube Channel CHANNELS DEMONETIZED OVER "DUPLICATE CONTENT" GLITCH | Big Problem for Creators https://www.youtube.com/watch?v=O58_BKHQxBw Please Subscribe and Follow Me @ Global Agenda Main Channel http://www.youtube.com/c/MarkCharles29 Global Agenda II http://www.youtube.com/c/GlobalAgenda Global Agenda on Twitter https://twitter.com/BD007Marky FAIR USE STATEMENT This video may contain copyrighted material the use of which has not been specifically authorized by the copyright owner. This material is being made available within this transformative or derivative work for the purpose of education, commentary and criticism, is being distributed without profit, and is believed to be "fair use" in accordance with Title 17 U.S.C. Section 107
Views: 338 Global Agenda
How to Apply Machine Learning (R,  Apache Spark, H2O.ai) To Real Time Streaming Analytics
 
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This video shows how business analysts, data scientists and developers work together to bring an analytic machine learning model into a (real time) production deployment. The beginning explains in two minutes the methodology before a 10min live demo discusses use cases such as customer churn and predictive analytics to demonstrate how different tooling for visual analytics / data discovery (TIBCO Spotfire), advanced analytics / machine learning (TIBCO Spotfire in conjunction with R, H2O.ai, Apache Spark) and stream processing / streaming analytics (TIBCO StreamBase, TIBCO Live Datamart) are combined by leveraging the same analytic model (e.g. clustering, random forest) without redevelopment. You are just beginning your journey with deploying analytic models to real time processing? Feel free to contact me to discuss your architecture, challenges and questions… If you want to discover some components by yourself, please check out our new and growing TIBCO Community Wiki (https://community.tibco.com/wiki). It already contains a lot of information about the discussed components, e.g. the page “Machine Learning in TIBCO Spotfire and TIBCO Streambase” (https://community.tibco.com/wiki/machine-learning-tibco-spotfirer-and-tibco-streambaser). You can also ask questions in the Answers section to get a response by a TIBCO expert or other community members (https://community.tibco.com/answers).
Views: 3920 Kai Wähner
HARD DRIVE Mining? This is getting ridiculous...
 
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Hard drive mining... could this be the solution to the GPU crisis?... For your unrestricted 30 days free trial, go to https://www.freshbooks.com/techtips and enter in “Linus Tech Tips” in the how you heard about us section. Get iFixit's Pro Tech Toolkit now for only $59.95 USD at https://www.ifixit.com/linus Buy HDDs on Amazon: http://geni.us/iJD6t Discuss on the forum: https://linustechtips.com/main/topic/910184-hard-drive-mining-this-is-getting-ridiculous/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/75969-linus-tech-tips-affiliates-referral-programs-and-sponsors Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips/ Linus Tech Tips posters at http://crowdmade.com/linustechtips Our production gear: http://geni.us/cvOS Get LTX 2018 tickets at https://www.ltxexpo.com/ Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxmFU3lWY iTunes Download Link: https://itunes.apple.com/us/album/supernova/id936805712 Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnirvana Sound effects provided by http://www.freesfx.co.uk/s
Views: 2319186 Linus Tech Tips
What is DATA WRANGLING? What does DATA WRANGLING mean? DATA WRANGLING meaning & explanation
 
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What is DATA WRANGLING? What does DATA WRANGLING mean? DATA WRANGLING meaning -DATA WRANGLING definition - DATA WRANGLING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data wrangling (sometimes referred to as data munging) is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. A data wrangler describes the person who performs these transformation operations. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. sorting) or parsing the data into predefined data structures, and finally depositing the resulting content into a data sink for storage and future use. The "wrangler" non-technical term is often said to derive from work done by the United States Library of Congress's National Digital Information Infrastructure and Preservation Program (NDIIPP) and their program partner the Emory University Libraries based MetaArchive Partnership. The term "mung" has roots in munging as described in the Jargon File. The term "Data Wrangler" was also suggested as the best analogy to coder for code for someone working with data. The terms data wrangling and data wrangler had sporadic use in the 1990s and early 2000s. One of the earliest business mentions of data wrangling was in an article in Byte Magazine in 1997 (Volume 22 issue 4) referencing “Perl’s data wrangling services”. In 2001 it was reported that CNN hired “a dozen data wranglers” to help track down information for news stories. One of the first mentions of data wrangler in a scientific context was by Donald Cline during the NASA/NOAA Cold Lands Processes Experiment. Cline stated the data wranglers “coordinate the acquisition of the entire collection of the experiment data.” Cline also specifies duties typically handled by a storage administrator for working with large amounts of data. This can occur in areas like major research projects and the making of films with a large amount of complex computer-generated imagery. In research, this involves both data transfer from research instrument to storage grid or storage facility as well as data manipulation for re-analysis via high performance computing instruments or access via cyberinfrastructure-based digital libraries. The data transformations are typically applied to distinct entities (e.g. fields, rows, columns, data values etc.) within a data set, and could include such actions as extractions, parsing, joining, standardizing, augmenting, cleansing, consolidating and filtering to create desired wrangling outputs that can be leveraged downstream. The recipients could be individuals, such as data architects or data scientists who will investigate the data further, business users who will consume the data directly in reports, or systems that will further process the data and write it into targets such as data warehouses, data lakes or downstream applications. Depending on the amount and format of the incoming data, data wrangling has traditionally been performed manually (e.g. via spreadsheets such as Excel) or via hand-written scripts in languages such as Python or SQL. R, a language often used in data mining and statistical data analysis, is now also often used for data wrangling. On a film or television production utilizing digital cameras that are not tape based, a data wrangler is employed to manage the transfer of data from a camera to a computer and/or hard drive.....
Views: 2920 The Audiopedia
Causes and Effects of Climate Change | National Geographic
 
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What causes climate change (also known as global warming)? And what are the effects of climate change? Learn the human impact and consequences of climate change for the environment, and our lives. ➡ Subscribe: http://bit.ly/NatGeoSubscribe About National Geographic: National Geographic is the world's premium destination for science, exploration, and adventure. Through their world-class scientists, photographers, journalists, and filmmakers, Nat Geo gets you closer to the stories that matter and past the edge of what's possible. Get More National Geographic: Official Site: http://bit.ly/NatGeoOfficialSite Facebook: http://bit.ly/FBNatGeo Twitter: http://bit.ly/NatGeoTwitter Instagram: http://bit.ly/NatGeoInsta Causes and Effects of Climate Change | National Geographic https://youtu.be/G4H1N_yXBiA National Geographic https://www.youtube.com/natgeo
Views: 496659 National Geographic
Learning from Bacteria about Social Networks
 
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Google Tech Talk (more info below) September 30, 2011 Presented by Eshel Ben-Jacob. ABSTRACT Scientific American placed Professor Eshel Ben-Jacob and Dr. Itay Baruchi's creation of a type of organic memory chip on its list of the year's 50 most significant scientific discoveries in 2007. For the last decade, he has pioneered the field of Systems Neuroscience, focusing first on investigations of living neural networks outside the brain. http://en.wikipedia.org/wiki/Eshel_Ben-Jacob Learning from Bacteria about Information Processing Bacteria, the first and most fundamental of all organisms, lead rich social life in complex hierarchical communities. Collectively, they gather information from the environment, learn from past experience, and make decisions. Bacteria do not store genetically all the information required to respond efficiently to all possible environmental conditions. Instead, to solve new encountered problems (challenges) posed by the environment, they first assess the problem via collective sensing, then recall stored information of past experience and finally execute distributed information processing of the 109-12 bacteria in the colony, thus turning the colony into super-brain. Super-brain, because the billions of bacteria in the colony use sophisticated communication strategies to link the intracellular computation networks of each bacterium (including signaling path ways of billions of molecules) into a network of networks. I will show illuminating movies of swarming intelligence of live bacteria in which they solve optimization problems for collective decision making that are beyond what we, human beings, can solve with our most powerful computers. I will discuss the special nature of bacteria computational principles in comparison to our Turing Algorithm computational principles, showing that we can learn from the bacteria about our brain, in particular about the crucial role of the neglected other side of the brain, distributed information processing of the astrocytes. Eshel Ben-Jacob is Professor of Physics of Complex Systems and holds the Maguy-Glass Chair in Physics at Tel Aviv University. He was an early leader in the study of bacterial colonies as the key to understanding larger biological systems. He maintains that the essence of cognition is rooted in the ability of bacteria to gather, measure, and process information, and to adapt in response. For the last decade, he has pioneered the field of Systems Neuroscience, focusing first on investigations of living neural networks outside the brain and later on analysis of actual brain activity. In 2007, Scientific American selected Ben-Jacob's invention, the first hybrid NeuroMemory Chip, as one of the 50 most important achievements in all fields of science and technology for that year. The NeuroMemory Chip entails imprinting multiple memories, based upon development of a novel, system-level analysis of neural network activity (inspired by concepts from statistical physics and quantum mechanics), ideas about distributed information processing (inspired by his research on collective behaviors of bacteria) and new experimental methods based on nanotechnology (carbon nanotubes). Prof. Ben-Jacob received his PhD in physics (1982) at Tel Aviv University, Israel. He served as Vice President of the Israel Physical Society (1999-2002), then as President of the Israel Physical Society (2002-2005), initiating the online magazine PhysicaPlus, the only Hebrew-English bilingual science magazine. The general principles he has uncovered have been examined in a wide range of disciplines, including their application to amoeboid navigation, bacterial colony competition, cell motility, epilepsy, gene networks, genome sequence of pattern-forming bacteria, network theory analysis of the immune system, neural networks, search, and stock market volatility and collapse. He has examined implications of bacterial collective intelligence for neurocomputing. His scientific findings have prompted studies of their implications for computing: using chemical "tweets" to communicate, millions of bacteria self-organize to form colonies that collaborate to feed and defend themselves, as in a sophisticated social network. This talk was hosted by Boris Debic, and arranged by Zann Gill and the Microbes Mind Forum.
Views: 27666 GoogleTechTalks
Perry Samson - Mining My Students Notes to Create Study Guides | Lectures On-Demand
 
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Professor Perry Samson, Atm, Oceanic & Space Sci. - CoE, University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Algorithm clarifies ‘big data’ clusters
 
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Rice University scientists have developed a big data technique that could have a significant impact on health care. The Rice lab of bioengineer Amina Qutub designed an algorithm called “progeny clustering” that is being used in a hospital study to identify which treatments should be given to children with leukemia. - See more at: http://news.rice.edu/2015/08/12/algorithm-clarifies-big-data-clusters-2/
Views: 1762 Rice University
Data Analytics Learn Python with Titanic dataset
 
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Learn Data analytics and learn python by exploring the titanic dataset. In this tutorial we use pandas to explore the titanic dataset. The data can be found here http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.xls If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer
Views: 1237 Python Programmer
How To Make Bitcoin With CCG Cloud Mining | Joining the CCG Mining Business Tutorial
 
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To Sign up with CCG Mining ►► https://tinyurl.com/ccgminingsignup Looking for accurate forex signals? See our in-depth reviews of the best forex signal providers HERE ►https://youtu.be/LK2VonA1H00 Easy Loophole To Turn $10 To $100,000? ====================================== This controversial video exposes the “hidden” method Russian traders turn $10 into $100,000. 93% of newbies who have tested this system confirm that the average ROI for them is 769% within just 3 weeks. This shocking method does not require a lot of money… Does not require prior experience… In fact, you can start tonight. Click here to watch how complete newbies too can turn $10 into $100,000 ► https://tinyurl.com/cryptocurrencymethod CCG Mining was founded in 2016 during the boom of cryptocurrencies. And today everything we do is based on the principle "We make it easy for you". CCG Mining - is an international company offering comprehensive solutions based on blockchain technology. So far, we have gained the confidence of 10 000 private clients and 135 business clients. Our branches are located in 6 offices in 5 countries: Poland, Great Britain, Russia, Latvia, Austria and Czech Republic. Our primary goal is to build the highest computing hashing power in Europe. Our mission is to provide professional services and consistently strive to achieve the greatest satisfaction of every customer. The foundation of our company is ripped, competent and highly qualified team. Thanks to this, we effectively deal with the changing market, with great flexibility by adapting working methods and technologies to the current needs and expectations of customers With pleasure we also accept new challenges while maintaining high quality and stability. CCG Mining is a cryptocurrency mining platform founded by CCG International LTD in 2016. The company operates two mining data centers in Poland dedicated to Bitcoin, Etherium, ZCash, Monero, Dass, LiteCoin and LBRY Credits. It’s a relatively new company, but is very transparent and has an ambitious goal of building the highest computing hashing power in Europe. If you visit their website, you will be able to see the facility and the entire team of blockchain specialists. To learn More and See CCG Mining Review click First link and to Visit Official Site Please Refer to Second Link. ►► http://forexgreentrading.blogspot.com/2018/01/how-to-make-bitcoin-with-best-cloud.html ►► https://tinyurl.com/ccgminingsignup Looking for accurate forex signals? See our in-depth reviews of the best forex signal providers HERE ► https://youtu.be/LK2VonA1H00 Easy Loophole To Turn $10 To $100,000? ====================================== This controversial video exposes the “hidden” method Russian traders turn $10 into $100,000. 93% of newbies who have tested this system confirm that the average ROI for them is 769% within just 3 weeks. This shocking method does not require a lot of money… Does not require prior experience… In fact, you can start tonight. Click here to watch how complete newbies too can turn $10 into $100,000 ► https://tinyurl.com/cryptocurrencymethod Powerful Platform, Enhanced Execution and Great Customer Service. Open Today. Types: CFDs for Gold, Indices, Commodities, Forex ► https://youtu.be/KHpoz1DHVEs ► https://youtu.be/xcYl8dN9DLc Related Search **************** bitcoin mining bitcoin mining software ccg bitcoin mining ccg bitcoin mining software how to start bitcoin mining bitcoin mining free ccg mining scam alert ccg mining review ccg mining sign up bitcoin mining hardware bitcoin mining computer bitcoin mining difficulty bitcoin mining pool bitcoin mining wiki bitcoin mining calculator bitcoin mining explained ccg bitcoin mining explained bitcoin mining rig is bitcoin mining profitable bitcoin miner app bitcoin miner hardware ccg bitcoin miner hardware bitcoin mining farm mine bitcoin how to bitcoin mine what is a bitcoin mining bitcoin mining company bitcoin mining requirements basics of bitcoin mining
Views: 176 Trading Station
Who's Bigger? A Quantitative Analysis of Historical Fame
 
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Google Tech Talk June 7, 2012 Presented by Steven Skiena. ABSTRACT A discipline of computational social science is emerging, applying large-scale text/data analysis to central problems in the humanities and social sciences. Here we study the problem of algorithmically-constructing quantitative measures of historical reputation. Who is more historically significant: Beethoven or Elvis? Washington or Lincoln? Newton or Einstein? Larry or Sergey? By exploiting large-scale data from several sources, we have developed a factor analysis-based ranking method which measures the relative importance of all the people described in Wikipedia in a rigorous way. We have validated our measure against published rankings of historical figures, demonstrating that our rankings are generally better than those of human experts. Our measure gives us the power to rigorously investigate several previously difficult-to-formalize questions, such as: -- Are the right people in the history books? -- How well do halls of fame correctly identify the most significant individuals? -- Are men and women treated equally in Wikipedia? -- Where can you donate money to maximize your personal fame? In this talk, I will discuss our methodology for ranking historical figures, with assessment results and applications. Our rankings are available for inspection at http://www.whoisbigger.com.
Views: 3683 GoogleTechTalks
Empowering SysAdmins by Radically Simplifying Root Cause Analysis with Loom Systems Ops
 
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Introducing Loom Systems Ops, the cutting-edge IT Operations Analytics solution that radically simplifies root cause analysis by automatically analyzing machine data in real-time. * Ingests every type of log file out of the box, including from applications which were developed in-house - zero configuration required from the user. * Applies machine learning algorithms to learn the unique signature of the data in your organization so that when an issue starts manifesting it is detected automatically in real time. * Uses advanced analysis algorithms to automatically point the user to the root cause of the issue. * Recommends a simple and actionable resolution to the issue, in plain English. Built for low-touch operational simplicity and usability, our solution empowers IT, DevOps, System Admins and NOC teams by helping them get better results in a fraction of the time. Predicting and resolving IT incidents is a breeze with Loom Systems Ops. Learn more: www.loomsystems.com
Views: 2372 Loom Systems
ROOT CANAL TREATMENT !!!
 
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ROOT CANAL TREATMENT !!! [ PART 1 ] Root canal treatment is needed when the pulp tissue is infected. It is a complicated procidure that has several steps! In this video you can see the part of the treatment where the pulp is devitalized. The nerve is being removed and in our next video you will see the final filling of the root canals. DENTAL ANESTHESIA TECHNIQUE - EXPLAINED : https://www.youtube.com/watch?v=ST9rtr9d9rw ROOT CANAL TREATMENT !!! [ PART 1 ] ------------------------------------------------------------------------------------------------------------------------------- If you like this video please don't forget to subscribe.Thank you :) FB Page : https://www.facebook.com/mr.dentisteducator/ Instagram : https://www.instagram.com/mr_dentist1/ Twitter : https://twitter.com/dentisteducator
Views: 1665418 Mr.Dentist
Cost Minimization of Big Data Processing in Geo distributed data centers
 
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TITLE: Cost Minimization of Big Data Processing in Geo distributed data centers DOMAIN: Big Data Abstract: Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are interesting because they often present a different set of problems for diabetic patient’s data. The research area to solve various problems and classification is one of main problem in the field. The research describes algorithmic discussion of J48, J48 Graft, Random tree, REP, LAD. Here used to compare the performance of computing time, correctly classified instances, kappa statistics, MAE, RMSE, RAE, RRSE and to find the error rate measurement for different classifiers in weka .In this paper the data classification is diabetic patients data set is developed by collecting data from hospital repository consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine tests. Weka tool is used to classify the data is evaluated using 10 fold cross validation and the results are compared. When the performance of algorithms, we found J48 is better algorithm in most of the cases. MICANS INFOTECH We Develop Final Year Projects in IEEE, ANDROID, BIGDATA, HADOOP, WEKA, NS2, NS3, VLSI, MATLAB, MECHANICAL, CIVIL. You can DOWNLOAD Basepaper and Abstract from our website http://www.micansinfotech.com/ Watch 2015-2016 Project Videos… IEEE 2015-2016 JAVA PROJECTS: http://www.micansinfotech.com/VIDEOS-2015-2016.html#CS-IEEE IEEE 2015-2016 DOTNET PROJECTS: http://www.micansinfotech.com/VIDEOS-2015-2016.html#CS-IEEE IEEE 2015-2016 NS2 PROJECTS: http://www.micansinfotech.com/VIDEOS-2015-2016.html#NS2-IEEE IEEE 2015-2016 NS3 PROJECTS: http://www.micansinfotech.com/VIDEOS-2015-2016.html#NS2-IEEE IEEE 2015-2016 MATLAB PROJECTS: http://www.micansinfotech.com/VIDEOS-2015-2016.html#MATLAB-IEEE IEEE 2015-2016 VLSI PROJECTS: http://www.micansinfotech.com/VIDEOS-2015-2016.html#VLSI-IEEE Application Projects Videos… APPLICATION PROJECTS: http://www.micansinfotech.com/VIDEO-APPLICATION-PROJECT.html PHP PROJECTS: http://www.micansinfotech.com/VIDEO-APPLICATION-PROJECT.html#PHP DOTNET APPLICATION PROJECTS: http://www.micansinfotech.com/VIDEO-APPLICATION-PROJECT.html ASP.NET APPLICATION PROJECTS: http://www.micansinfotech.com/VIDEO-APPLICATION-PROJECT.html#ASP-APPLICATION VB.NET APPLICATION PROJECTS: http://www.micansinfotech.com/VIDEO-APPLICATION-PROJECT.html#VB_NET C# APPLICATION PROJECTS: http://www.micansinfotech.com/VIDEO-APPLICATION-PROJECT.html#CSHARP Output Videos… IEEE PROJECTS: https://www.youtube.com/channel/UCTgsK0GU0obcXKVaQsMAlAg/videos NS2 PROJECTS: https://www.youtube.com/channel/UCS-GYmNKbWSNLdcqxXcr_mw/videos NS3 PROJECTS: https://www.youtube.com/channel/UCBzmrzd3VxQWRulpZzA90Dw MATLAB PROJECTS: https://www.youtube.com/channel/UCK0ZyBsBUtan75ESp7Jqtmg/videos VLSI PROJECTS: https://www.youtube.com/channel/UCe0tzjvy9CGKa7zFvi6y3VQ/videos IEEE JAVA PROJECTS: https://www.youtube.com/channel/UCSCmROz5TcZp_GXby_yCAnw/videos IEEE DOTNET PROJECTS: https://www.youtube.com/channel/UCSCmROz5TcZp_GXby_yCAnw/videos APPLICATION PROJECTS: https://www.youtube.com/channel/UCVO9JhBXLFCwXtGamLUxrOw/videos PHP PROJECTS: https://www.youtube.com/channel/UCVO9JhBXLFCwXtGamLUxrOw/videos MICANS INFOTECH Senthilkumar B.Tech Director Micans Infotech Phone No: +91-9003628940 CORPORATE OFFICE #8, 100 Feet Road, At Indira Gandhi Square Opp to Hotel Aboorva PUDUCHERRY, INDIA +91 90036 28940 BRANCH OFFICE 798-C, Nehruji Road, I Floor Opp to Rice Committee VILLUPURAM, INDIA +91 94435 11725 URL: www.micansinfotech.com MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHILL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM COMPANY PROJECTS, INTERNSHIP TRAINING, MECHANICAL PROJECTS, ANSYS PROJECTS, CAD PROJECTS, CAE PROJECTS, DESIGN PROJECTS, CIVIL PROJECTS, IEEE MCA PROJECTS, IEEE M.TECH PROJECTS, IEEE PROJECTS, IEEE PROJECTS IN PONDY, IEEE PROJECTS, EMBEDDED PROJECTS, ECE PROJECTS PONDICHERRY, DIPLOMA PROJECTS, FABRICATION PROJECTS, IEEE PROJECTS CSE, IEEE PROJECTS CHENNAI, IEEE PROJECTS CUDDALORE, IEEE PROJECTS VILLUPURAM, IEEE PROJECTS IN PONDICHERRY, PROJECT DEVELOPMENT
Hadoop Vs Traditional Database Systems | Hadoop Data Warehouse | Hadoop and ETL | Hadoop Data Mining
 
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http://www.edureka.co/hadoop Email Us: [email protected],phone : +91-8880862004 This short video explains the problems with existing database systems and Data Warehouse solutions, and how Hadoop based solutions solves these problems. Let's Get Going on our Hadoop Journey and Join our 'Big Data and Hadoop' course. - - - - - - - - - - - - - - How it Works? 1. This is a 10-Module Instructor led Online Course. 2. We have a 3-hour Live and Interactive Sessions every Sunday. 3. We have 4 hours of Practical Work involving Lab Assignments, Case Studies and Projects every week which can be done at your own pace. We can also provide you Remote Access to Our Hadoop Cluster for doing Practicals. 4. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 5. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, MapReduce, Advance MapReduce, PIG, HIVE, HBase, Zookeeper, SQOOP, Hadoop 2.0 , YARN etc. will be covered in the course. - - - - - - - - - - - - - - Course Objectives After the completion of the Hadoop Course at Edureka, you should be able to: Master the concepts of Hadoop Distributed File System. Understand Cluster Setup and Installation. Understand MapReduce and Functional programming. Understand How Pig is tightly coupled with Map-Reduce. Learn how to use Hive, How you can load data into HIVE and query data from Hive. Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing. Have a good understanding of ZooKeeper service and Sqoop, Hadoop 2.0, YARN, etc. Develop a working Hadoop Architecture. - - - - - - - - - - - - - - Who should go for this course? This course is designed for developers with some programming experience (preferably Java) who are looking forward to acquire a solid foundation of Hadoop Architecture. Existing knowledge of Hadoop is not required for this course. - - - - - - - - - - - - - - Why Learn Hadoop? BiG Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop, which is not less than a panacea for all those companies working with BIG DATA in a variety of applications and has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Some of the top companies using Hadoop: The importance of Hadoop is evident from the fact that there are many global MNCs that are using Hadoop and consider it as an integral part of their functioning, such as companies like Yahoo and Facebook! On February 19, 2008, Yahoo! Inc. established the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on over 10,000 core Linux cluster and generates data that is now widely used in every Yahoo! Web search query. Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! Happy Hadooping! Please write back to us at [email protected] or call us at +91-8880862004 for more information. http://www.edureka.co/big-data-and-hadoop
Views: 13967 edureka!
Data Analytics: The Big Picture - Clarence
 
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Data Analytics, a deeper insight into this topic and how Zoho Reports goes a long way in helping address day to day business issues in this particular domain. Online reporting and business intelligence service. Sign up for free: http://zoho.com/reports Solutions: https://www.zoho.com/reports/solutions.html Connect with Zoho Twitter: https://twitter.com/zoho
Views: 727 Zoho
Web-based Inference Detection
 
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Google Tech Talk February 13, 2009 ABSTRACT Presented by Jessica Staddon. Text content can allow unintended inferences. Consider, for example, the numerous people who have published anonymous blogs for venting about their employer only to be identified through seemingly non-identifying posts. Similarly, the US government's "Operation Iraqi Freedom Portal" was assembled as evidence of nuclear weapons presence in Iraq, but removed because it could be used to infer much of the weapon making process. We propose a simple, semi-automated approach to detecting text-based inferences prior to the release of content. Our approach uses association rule mining of the Web to identify keywords that may allow a sensitive topic to be inferred. While the main application of this work is data leak prevention we will also discuss how it might be used to detect bias in product reviews. Finally, if time permits, we will discuss how inference detection can support topic-driven access control. Most of this talk is joint work with Richard Chow and Philippe Golle. Jessica is an area manager at PARC (aka Xerox PARC). She received her PhD in Math from U. C. Berkeley and has held research scientist positions at RSA Labs and Bell Labs. Jessica's background is in applied cryptography, specifically, cryptographic protocols for large, dynamic groups. Her current research interests include the use of data mining to support content privacy. http://www.parc.com/jstaddon
Views: 2737 GoogleTechTalks
Digital Transformation: Steve Wilson, "Silicon Valley Has Forgotten Computer Science 101"
 
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See more clips and interviews at http://www.digitaltransformation-film.com Steve Wilson: "Data mining and machine learning, machine decision-making, augmented decision-making, we've had some of this stuff for twenty years in health care. There's been the idea of decision support systems and now they're emerging. Let's call it AI. When you fire up your map application, and it knows we where you're probably going from the time of day, and without prompting it gives you a little bit of advice about what traffic jam to avoid. So that's AI. We easily then slide into an optimistic view that cars are going to become self-driving. If you go back to the very first days of personal computing, people thought that the personal computer was going to transform the way we live overnight. Again, we underestimate what could be done in ten years we get carried away in the first year. I think it's really important that the claims that go behind things like self-driving cars and big AI, the idea that you could have a human-like awareness in computers, we really fundamentally need to revisit some of that stuff. I don't know what they teach in schools these days, but when I did first year computer science in the 1970s, we were taught about fundamental limits of algorithms. There are some things that algorithms can't do. Mathematicians will tell you there's some things that algorithms will never do. I think that we've forgotten this collectively. I think Silicon Valley has forgotten computer science 101. It's hearing down this path of assuming that self-driving cars are just around the corner. There have been some horrible missteps in machine vision and object classification, [for example] the famous racist algorithms. Now this is not just a matter of the programmers, of the developers' bias infecting their work. That's inevitable, and some good analysis is coming out now, some good ways of moderating, and helping people be more responsible in the design of algorithms. It's beyond that, it's about an optimism that's infected AI itself, that the self-driving car is going to be human-like. You know, a computer can't even solve the traveling salesperson problem; a computer cannot tell you the fastest way to get from A to B via every town in a in a country. What chances is there really that a car is going to be fully functional and it's going to be delegated full responsibility to make life-and-death decisions? People are talking about life-and-death decisions already, you know, the famous trolley problem. I've seen engineers clearly for the very first time rush off to Wikipedia to look up the trolley problem (https://en.wikipedia.org/wiki/Trolley_problem). The thing about the trolley problem is that it has no resolution. Ethicists and philosophers will tell you that the point, the moral of the story, is that there is no answer to the trolley problem. It can't be programmed. We cannot have a program running a self-driving car that's going to come up the right answer to the trolley problem every single time. The engineers are going along as if the trolley problem is just an algorithm problem." Steve Wilson is a Digital Identity Innovator & Analyst at Constellation Research. http://www.constellationr.com "Digital Transformation: Visions of Nations, Companies, and People" is a film by Manuel Stagars.
Views: 60 Manuel Stagars
Boomerang Trick Shots | Dude Perfect
 
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Time to take boomerangs to the next level! ► Click HERE to subscribe to Dude Perfect! http://bit.ly/SubDudePerfect ► Click HERE to watch our most recent videos! http://bit.ly/NewestDudePerfectVideos http://bit.ly/NewestDPVideos ►Click HERE to follow Logan on Instagram! Follow @logan.broadbent: https://www.instagram.com/logan.broadbent ► SHOP our NEW Merchandise! - http://bit.ly/DPStore ►Click HERE to join the exclusive Dude Perfect T-Shirt Club! http://bit.ly/DPTShirtClub Music: Army by Zayde Wolf ►Click HERE to download : http://smarturl.it/ZWGoldenAge Play our NEW iPhone game! ► PLAY Endless Ducker on iPhone -- http://smarturl.it/EndlessDucker ► PLAY Endless Ducker on Android -- http://smarturl.it/EndlessDucker ► VISIT our NEW STORE - http://bit.ly/DPStore ► JOIN our NEWSLETTER - http://bit.ly/DPNewsletterEndCard ► WATCH our STEREOTYPES - http://bit.ly/StereotypesPlaylist In between videos we hang out with you guys on Instagram, Snapchat, Twitter, and Facebook so pick your favorite one and hang with us there too! http://Instagram.com/DudePerfect http://bit.ly/DudePerfectSnapchat http://Twitter.com/DudePerfect http://Facebook.com/DudePerfect Do you have a GO BIG mindset? See for yourself in our book "Go Big." ►http://amzn.to/OYdZ2s A special thanks to those of you who play our iPhone Games and read our book. You guys are amazing and all the great things you tell us about the game and the book make those projects so worthwhile for us! Dude Perfect GAME - http://smarturl.it/DPGameiPhone Dude Perfect BOOK - "Go Big" - http://amzn.to/OYdZ2s Click here if you want to learn more about Dude Perfect: http://www.dudeperfect.com/blog-2/ Bonus points if you're still reading this! Comment As always...Go Big and God Bless! - Your friends at Dude Perfect Business or Media, please contact us at: [email protected] ------------ 5 Best Friends and a Panda. If you like Sports + Comedy, come join the Dude Perfect team! Best known for trick shots, stereotypes, battles, bottle flips, ping pong shots and all around competitive fun, Dude Perfect prides ourselves in making the absolute best family-friendly entertainment possible! Welcome to the crew! Pound it. Noggin. - Dude Perfect
Views: 45761376 Dude Perfect
What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning & explanation
 
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What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning - CLUSTER ANALYSIS definition - CLUSTER ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek ß????? "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals. Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.
Views: 5816 The Audiopedia

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