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Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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Part 7 of this video series includes specific coverage of: – The trade-offs of expanding the text analytics feature space with n-grams. – How bag-of-words representations map to the vector space model (VSM). – Usage of the dot product between document vectors as a proxy for correlation. – Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. – How LSA is implemented using singular value decomposition (SVD). – Mapping new data into the lower dimensional SVD space. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JVc0 See what our past attendees are saying here: https://hubs.ly/H0f5K6Q0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 11885 Data Science Dojo
Word Embeddings
 
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Word embeddings are one of the coolest things you can do with Machine Learning right now. Try the web app: https://embeddings.macheads101.com Word2vec paper: https://arxiv.org/abs/1301.3781 GloVe paper: https://nlp.stanford.edu/pubs/glove.pdf GloVe webpage: https://nlp.stanford.edu/projects/glove/ Other resources: http://www.aclweb.org/anthology/Q15-1016 https://en.wikipedia.org/wiki/Word_embedding
Views: 57521 macheads101
Information Retrieval WS 17/18, Lecture 10: Latent Semantic Indexing
 
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This is the recording of Lecture 10 from the course "Information Retrieval", held on 9th January 2018 by Prof. Dr. Hannah Bast at the University of Freiburg, Germany. The discussed topics are: Latent Semantic Indexing, Matrix Factorization, Singular Value Decomposition (SVD), Eigenvector Decomposition (EVD). Link to the Wiki of the course: https://ad-wiki.informatik.uni-freiburg.de/teaching/InformationRetrievalWS1718 Link to the homepage of our chair: https://ad.informatik.uni-freiburg.de/
Views: 2743 AD Lectures
What is TOPIC MODEL? What does TOPIC MODEL mean? TOPIC MODEL meaning, definition & explanation
 
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What is TOPIC MODEL? What does TOPIC MODEL mean? TOPIC MODEL meaning - TOPIC MODEL definition - TOPIC MODEL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. The "topics" produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's balance of topics is. Topic models are also referred to as probabilistic topic models, which refers to statistic algorithms for discovering the latent semantic structures of an extensive text body. In the age of information, the amount of the written material we encounter each day is simply beyond our processing capacity. Topic models can help to organize and offer insights for us to understand large collections of unstructured text bodies. Originally developed as a text-mining tool, topic models have been used to detect instructive structures in data such as genetic information, images, and networks. They also have applications in other fields such as bioinformatics. Topic models can include context information such as timestamps, authorship information or geographical coordinates associated with documents. Additionally, network information (such as social networks between authors) can be modelled. Approaches for temporal information include Block and Newman's determination the temporal dynamics of topics in the Pennsylvania Gazette during 1728–1800. Grif?ths & Steyvers use topic modeling on abstract from the journal PNAS to identify topics that rose or fell in popularity from 1991 to 2001. Nelson has been analyzing change in topics over time in the Richmond Times-Dispatch to understand social and political changes and continuities in Richmond during the American Civil War. Yang, Torget and Mihalcea applied topic modeling methods to newspapers from 1829–2008. Mimno used topic modelling with 24 journals on classical philology and archaeology spanning 150 years to look at how topics in the journals change over time and how the journals become more different or similar over time. Yin et al. introduced a topic model for geographically distributed documents, where document positions are explained by latent regions which are detected during inference. Chang and Blei included network information between linked documents in the relational topic model, which allows to model links between websites. The author-topic model by Rosen-Zvi et al. models the topics associated with authors of documents to improve the topic detection for documents with authorship information. In practice researchers attempt to fit appropriate model parameters to the data corpus using one of several heuristics for maximum likelihood fit. A recent survey by Blei describes this suite of algorithms. Several groups of researchers starting with Papadimitriou et al. have attempted to design algorithms with probable guarantees. Assuming that the data were actually generated by the model in question, they try to design algorithms that probably find the model that was used to create the data. Techniques used here include singular value decomposition (SVD) and the method of moments. In 2012 an algorithm based upon non-negative matrix factorization (NMF) was introduced that also generalizes to topic models with correlations among topics.
Views: 3618 The Audiopedia
What is LATENT SEMANTIC MAPPING? What does LATENT SEMANTIC MAPPING mean?
 
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What is LATENT SEMANTIC MAPPING? What does LATENT SEMANTIC MAPPING mean? LATENT SEMANTIC MAPPING meaning - LATENT SEMANTIC MAPPING definition - LATENT SEMANTIC MAPPING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Latent semantic mapping (LSM) is a data-driven framework to model globally meaningful relationships implicit in large volumes of (often textual) data. It is a generalization of latent semantic analysis. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. LSM was derived from earlier work on latent semantic analysis. There are 3 main characteristics of latent semantic analysis: Discrete entities, usually in the form of words and documents, are mapped onto continuous vectors, the mapping involves a form of global correlation pattern, and dimensionality reduction is an important aspect of the analysis process. These constitute generic properties, and have been identified as potentially useful in a variety of different contexts. This usefulness has encouraged great interest in LSM. The intended product of latent semantic mapping, is a data-driven framework for modeling relationships in large volumes of data. Mac OS X v10.5 and later includes a framework implementing latent semantic mapping.
Views: 211 The Audiopedia
LSA LUHN EDMONDSON  |  Text Summarization |   NLP
 
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======== Code ============ from __future__ import absolute_import from __future__ import division, print_function, unicode_literals from sumy.parsers.html import HtmlParser from sumy.parsers.plaintext import PlaintextParser from sumy.nlp.tokenizers import Tokenizer from sumy.nlp.stemmers import Stemmer from sumy.utils import get_stop_words from sumy.summarizers.lsa import LsaSummarizer from sumy.summarizers.luhn import LuhnSummarizer from sumy.summarizers.edmundson import EdmundsonSummarizer LANGUAGE = "english" SENTENCES_COUNT = 2 if __name__ == "__main__": url = "https://en.wikipedia.org/wiki/Deep_learning" parser = HtmlParser.from_url(url, Tokenizer(LANGUAGE)) print("====================== LSA SUMMARIZER ==========================") lsa_summarizer = LsaSummarizer(Stemmer(LANGUAGE)) lsa_summarizer.stop_words = get_stop_words(LANGUAGE) for sentence in lsa_summarizer(parser.document, SENTENCES_COUNT): print(sentence) print("\n\n====================== LUHN SUMMARIZER ==========================") luhn_summarizer = LuhnSummarizer() luhn_summarizer.stop_words = get_stop_words(LANGUAGE) for sentence in luhn_summarizer(parser.document, SENTENCES_COUNT): print(sentence) print("\n\n====================== EDMUNDSON SUMMARIZER ==========================") edm_summarizer = EdmundsonSummarizer() edm_summarizer.bonus_words = ("deep", "learning", "neural") edm_summarizer.stigma_words = ("another", "and", "some", "next",) edm_summarizer.null_words = ("another", "and", "some", "next",) for sentence in edm_summarizer(parser.document, SENTENCES_COUNT): print(sentence)
Views: 50 Naman Adep
Gilbert Strang
 
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If you find our videos helpful you can support us by buying something from amazon. https://www.amazon.com/?tag=wiki-audio-20 Gilbert Strang William Gilbert Strang (born November 27, 1934 in Chicago), usually known as simply Gilbert Strang or Gil Strang, is an American mathematician, with contributions to finite element theory, the calculus of variations, wavelet analysis and linear algebra.He has made many contributions to mathematics education, including publishing seven mathematics textbooks and one monograph. -Video is targeted to blind users Attribution: Article text available under CC-BY-SA image source in video https://www.youtube.com/watch?v=zCl8qdvfDH8
Views: 191 WikiAudio
Apertium Tutorial - Disambiguate Text (apertium-eng)
 
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A tutorial for disambiguating text for Apertium. Links: Install Apertium - http://wiki.apertium.org/wiki/Installation apertium-eng on GitHub - https://github.com/apertium/apertium-eng
Views: 111 Andi Qu
sfspark.org: Sandy Ryza, Semantic Indexing of Four Million Documents with Spark
 
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Latent Semantic Analysis (LSA) is a technique in natural language processing and information retrieval that seeks to better understand the latent relationships and concepts in large corpuses. In this talk, we’ll walk through what it looks like to apply LSA to the full set of documents in English Wikipedia, using Apache Spark. Harnessing the Stanford CoreNLP library for lemmatization and MLlib’s scalable SVD implementation for uncovering a lower-dimensional representation of the data, we’ll undertake the modest task of enabling queries against the full extent of human knowledge, based on latent semantic relationships. Sandy Ryza is a senior data scientist at Cloudera focusing on Apache Spark and its ecosystem, and an author of the O’Reilly book Advanced Analytics with Spark. He is a Spark committer and member of the Apache Hadoop project management committee. He graduated Phi Beta Kappa from Brown University. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 656 FunctionalTV
Free LSI Keyword Search Tool For Latent Semantic Indexing
 
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Free LSI Keyword Search Tool For Latent Semantic Indexing https://www.youtube.com/watch?v=M4fCKJB6i7E Looking For A Good Keyword Tool? https://www.youtube.com/watch?v=spl0u0iMy0o https://www.youtube.com/watch?v=NdHogIxI0VU https://www.youtube.com/watch?v=ida_Vs3uNZI https://www.youtube.com/watch?v=D6MjLO-tUcQ More Information about Latent Semantic Indexing: Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysisWikipedia Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysis Wikipedia Jump to Benefits of LSI - Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. ‎Overview · ‎Applications · ‎Implementation · ‎Limitations Latent Semantic Indexing - SEO Book www.seobook.com › LSI Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines the document collection as a whole, to see which other documents contain some of those same words. How LSI Works - SEO Book www.seobook.com › LSI We mentioned that latent semantic indexing looks at patterns of word distribution (specifically, word co-occurence) across a set of documents. Before we talk ... Latent semantic indexing - The Stanford Natural Language Processing ... nlp.stanford.edu/IR-book/html/.../latent-semantic-indexing-1.html Next: References and further reading Up: Matrix decompositions and latent ... This process is known as latent semantic indexing (generally abbreviated LSI). What Is Latent Semantic Indexing - Search Engine Journal https://www.s Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysis Wikipedia Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of ... ‎Overview · ‎Applications · ‎Implementation · ‎Limitations Semantic analysis (machine learning) - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Semantic_analysis_(machine_lear... Wikipedia In machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Latent semantic analysis (sometimes latent semantic indexing), is a class of ... Probabilistic latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_ana... Wikipedia Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing is a statistical technique for the analysis of two-mode and ... Latent Semantic Indexing - SEO Book http://www.seobook.com › LSI Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method ... Latent Semantic Indexing http://c2.com/cgi/wiki?LatentSemanticIndexing Latent semantic indexing adds an important step to the document indexing process. In addition to record People who watched this video: https://youtu.be/M4fCKJB6i7E Also searched online for: Searches related to Latent Semantic Indexing latent semantic indexing tutorial latent semantic indexing seo latent semantic indexing tool latent semantic indexing example latent semantic indexing python latent semantic indexing r latent semantic indexing ppt latent semantic indexing java Don't forget to check out our YouTube Channel: https://www.youtube.com/user/oneclicklearning and click the link below to subscribe to our channel and get informed when we add new content: https://www.youtube.com/user/oneclicklearning -------------------------------------------- #latentsemanticindexingtutorial #latentsemanticindexingseo #latentsemanticindexingtool #latentsemanticindexingexample #latentsemanticindexingpython #latentsemanticindexingr #latentsemanticindexingppt #latentsemanticindexingjava --------------------------------------------
Views: 728 Chet Hastings
Applying Semantic Analyses to Content-based Recommendation and Document Clustering
 
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This talk will present the results of my research on feature generation techniques for unstructured data sources. We apply Probase, a Web-scale knowledge base developed by Microsoft Research Asia, which is generated from the Bing index, search query logs and other sources, to extract concepts from text. We compare the performance of features generated from Probase and two other forms of semantic analysis, Explicit Semantic Analysis using Wikipedia and Latent Dirichlet Allocation. We evaluate the semantic analysis techniques on two tasks, recommendation using Matchbox, which is a platform for probabilistic recommendations from Microsoft Research Cambridge, and clustering using K-Means.
Views: 778 Microsoft Research
What is LATENT SEMANTIC INDEXING? What does LATENT SEMANTIC INDEXING mean?
 
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What is LATENT SEMANTIC INDEXING? What does LATENT SEMANTIC INDEXING mean? LATENT SEMANTIC INDEXING meaning - LATENT SEMANTIC INDEXING definition - LATENT SEMANTIC INDEXING 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 Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. LSI is based on the principle that words that are used in the same contexts tend to have similar meanings. A key feature of LSI is its ability to extract the conceptual content of a body of text by establishing associations between those terms that occur in similar contexts. LSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents. Called Latent Semantic Indexing because of its ability to correlate semantically related terms that are latent in a collection of text, it was first applied to text at Bellcore in the late 1980s. The method, also called latent semantic analysis (LSA), uncovers the underlying latent semantic structure in the usage of words in a body of text and how it can be used to extract the meaning of the text in response to user queries, commonly referred to as concept searches. Queries, or concept searches, against a set of documents that have undergone LSI will return results that are conceptually similar in meaning to the search criteria even if the results don’t share a specific word or words with the search criteria.
Views: 949 The Audiopedia
Machine Reading with Word Vectors (ft. Martin Jaggi)
 
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This video discusses how to represent words by vectors, as prescribed by word2vec. It features Martin Jaggi, Assistant Professor of the IC School at EPFL. https://people.epfl.ch/martin.jaggi Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean (2013). Efficient Estimation of Word Representations in Vector Space. https://arxiv.org/pdf/1301.3781v3.pdf Omar Levy and Yoav Goldberg (2014). Neural Word Embedding as Implicit Matrix Factorization. https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf
Views: 22353 ZettaBytes, EPFL
Semantic analysis Meaning
 
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Video shows what semantic analysis means. The process of relating syntactic structures, from the levels of phrases, clauses, sentences and paragraphs to the level of the writing as a whole, to their language-independent meanings, removing features specific to particular linguistic and cultural contexts, to the extent that such a project is possible.. The phase in which a compiler adds semantic information to the parse tree and builds the symbol table.. Semantic analysis Meaning. How to pronounce, definition audio dictionary. How to say semantic analysis. Powered by MaryTTS, Wiktionary
Views: 1222 ADictionary
Word2vec with Gensim - Python
 
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This video explains word2vec concepts and also helps implement it in gensim library of python. Word2vec extracts features from text and assigns vector notations for each word. The word relations are preserved using this. A famous result of word2vec is King - Man + Woman = Queen . This concept has lots other applications as well. Gensim is a library in python which is used to create word2vec models for your corpus. We Learn CBOW- Continuous bowl of words and Skip Gram models to get an intuition about word2vec. Download pretrained word2vec models : https://github.com/jhlau/doc2vec Dataset : https://www.kaggle.com/jiriroz/qa-jokes Find the code GitHub: https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Recommended book for Deep Learning : http://amzn.to/2nXweQS
Views: 72899 The Semicolon
Information Retrieval WS 17/18, Lecture 8: Vector Space Model
 
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This is the recording of Lecture 8 from the course "Information Retrieval", held on 12th December 2017 by Prof. Dr. Hannah Bast at the University of Freiburg, Germany. The discussed topic is: Character Encoding, Unicode, UTF-8, UTF-16, UTF-32, Vector Space Model (VSM). Link to the Wiki of the course: https://ad-wiki.informatik.uni-freiburg.de/teaching/InformationRetrievalWS1718 Link to the homepage of our chair: https://ad.informatik.uni-freiburg.de/
Views: 942 AD Lectures
What is AUTOMATED ESSAY SCORING? What does AUTOMATED ESSAY SCORING mean?
 
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What is AUTOMATED ESSAY SCORING? What does AUTOMATED ESSAY SCORING mean? AUTOMATED ESSAY SCORING meaning - AUTOMATED ESSAY SCORING definition - AUTOMATED ESSAY SCORING 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 Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a method of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades—for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification. Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost. The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test). From the beginning, the basic procedure for AES has been to start with a training set of essays that have been carefully hand-scored. The program evaluates surface features of the text of each essay, such as the total number of words, the number of subordinate clauses, or the ratio of uppercase to lowercase letters - quantities that can be measured without any human insight. It then constructs a mathematical model that relates these quantities to the scores that the essays received. The same model is then applied to calculate scores of new essays. Recently, one such mathematical model was created by Isaac Persing and Vincent Ng. which not only evaluates essays on the above features, but also on their argument strength. It evaluates various features of the essay, such as the agreement level of the author and reasons for the same, adherence to the prompt's topic, locations of argument components (major claim, claim, premise), errors in the arguments, cohesion in the arguments among various other features. In contrast to the other models mentioned above, this model is closer in duplicating human insight while grading essays. The various AES programs differ in what specific surface features they measure, how many essays are required in the training set, and most significantly in the mathematical modeling technique. Early attempts used linear regression. Modern systems may use linear regression or other machine learning techniques often in combination with other statistical techniques such as latent semantic analysis and Bayesian inference. Any method of assessment must be judged on validity, fairness, and reliability. An instrument is valid if it actually measures the trait that it purports to measure. It is fair if it does not, in effect, penalize or privilege any one class of people. It is reliable if its outcome is repeatable, even when irrelevant external factors are altered. Before computers entered the picture, high-stakes essays were typically given scores by two trained human raters. If the scores differed by more than one point, a third, more experienced rater would settle the disagreement. In this system, there is an easy way to measure reliability: by inter-rater agreement. If raters do not consistently agree within one point, their training may be at fault. If a rater consistently disagrees with whichever other raters look at the same essays, that rater probably needs more training. Various statistics have been proposed to measure inter-rater agreement. Among them are percent agreement, Scott's ?, Cohen's ?, Krippendorf's ?, Pearson's correlation coefficient r, Spearman's rank correlation coefficient ?, and Lin's concordance correlation coefficient. Percent agreement is a simple statistic applicable to grading scales with scores from 1 to n, where usually 4 ? n ? 6. It is reported as three figures, each a percent of the total number of essays scored: exact agreement (the two raters gave the essay the same score), adjacent agreement (the raters differed by at most one point; this includes exact agreement), and extreme disagreement (the raters differed by more than two points). Expert human graders were found to achieve exact agreement on 53% to 81% of all essays, and adjacent agreement on 97% to 100%.....
Views: 553 The Audiopedia
Lecture 2 | Word Vector Representations: word2vec
 
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Lecture 2 continues the discussion on the concept of representing words as numeric vectors and popular approaches to designing word vectors. Key phrases: Natural Language Processing. Word Vectors. Singular Value Decomposition. Skip-gram. Continuous Bag of Words (CBOW). Negative Sampling. Hierarchical Softmax. Word2Vec. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
4 Methodology 2: Data cleaning, Principal Component Analysis, Eigenfaces (MLVU2019)
 
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slides: https://mlvu.github.io/lectures/22.Methodology2.annotated.pdf course materials: https://mlvu.github.io In this lecture we discuss how to prepare your data for machine learning project. In the second half we discuss PCA, a dimensionality reduction method which is both a good method for preparing your data, but also quite a powerful method to analyse your data and exposing its structure. Note: the "clever man" who exposed the survivorship bias in the analysis of aircraft damage was Abraham Wald: https://en.wikipedia.org/wiki/Abraham_Wald
Views: 548 Peter Bloem
Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14
 
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A visual understanding of eigenvectors, eigenvalues, and the usefulness of an eigenbasis. Full series: http://3b1b.co/eola Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. http://3b1b.co/support Typo: At 12:27, "more that a line full" should be "more than a line full". ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: https://goo.gl/WmnCQZ Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 977430 3Blue1Brown
Numerical linear algebra
 
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If you find our videos helpful you can support us by buying something from amazon. https://www.amazon.com/?tag=wiki-audio-20 Numerical linear algebra Numerical linear algebra is the study of algorithms for performing linear algebra computations, most notably matrix operations, on computers.It is often a fundamental part of engineering and computational science problems, such as image and signal processing, telecommunication, computational finance, materials science simulations, structural biology, data mining, bioinformatics, fluid dynamics, and many other areas. -Video is targeted to blind users Attribution: Article text available under CC-BY-SA image source in video https://www.youtube.com/watch?v=2YrVbWBoSuE
Views: 149 WikiAudio
How to remove noise from noisy signal in Matlab?
 
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This tutorial video teaches about removing noise from noisy signal using band pass butterworth signal. you can also download the code here at: http://www.jcbrolabs.org/matlab-codes
Views: 18515 sachin sharma
Applications of Recommender Systems
 
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Εφαρμογές συστημάτων συστάσεων Το βίντεο αυτό αναφέρεται στα συστήματα συστάσεων που βοηθούν τους καταναλωτές κάνοντας προτάσεις για να βοηθήσουν τους χρήστες να πάρουν μια απόφαση για ένα προϊόν. Οι εφαρμογές που λειτουργούν με αυτά τα συστήματα αυτά συνήθως χρησιμοποιούν διάφορες προσεγγίσεις. Μερικές πολύ γνωστές εφαρμογές είναι το Amazon, το Netflix, το Pandora, το eBay και το Reel.com. Πηγές: Schafer, JB., &  Konstan ,J., Riedi, J.(1999). Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce (EC '99). ACM, New York, NY, USA, 158-166. Retrieved 04 December, 2012 from http://www.win.tue.nl/~laroyo/2L340/resources/recommender-systems-e-commerce.pdf Segaran, T. (2007). Programming Collective Intelligence: Building Smart Web 2.0 Applications. Sebastopol :O'Reilly. http://en.wikipedia.org Made by: Maria Georgiou
Views: 683 cis405
Mod-01 Lec-32 Word Sense Disambiguation
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 2592 nptelhrd
Lecture 3 | GloVe: Global Vectors for Word Representation
 
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Lecture 3 introduces the GloVe model for training word vectors. Then it extends our discussion of word vectors (interchangeably called word embeddings) by seeing how they can be evaluated intrinsically and extrinsically. As we proceed, we discuss the example of word analogies as an intrinsic evaluation technique and how it can be used to tune word embedding techniques. We then discuss training model weights/parameters and word vectors for extrinsic tasks. Lastly we motivate artificial neural networks as a class of models for natural language processing tasks. Key phrases: Global Vectors for Word Representation (GloVe). Intrinsic and extrinsic evaluations. Effect of hyperparameters on analogy evaluation tasks. Correlation of human judgment with word vector distances. Dealing with ambiguity in word using contexts. Window classification. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://online.stanford.edu/
Visual and semantic analytics on Tweets in a web environment
 
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Luciad, Dataiku and HP Enterprise built a demonstrator based on geolocated tweets in order to dynamically identify and visualize topics of discussions, groups of users and "contacts." This is demonstrating the ability to understand, focus, and track targets by running complex analysis like topic modeling, users clustering and graph analytics handling spatial data computation. In this video, you will understand how to leverage from spatial and unstructured data to analyze and find hidden relationships in data and between users. This demonstrator is developed with the best-in-class solutions HPE Vertica, LuciadRIA and Dataiku Data Science Studio. LuciadRIA is used for the visual front-end and allows GPU-accelerated filtering on large amounts of twitter data.
Views: 297 LUCIAD
Topic mining with LDA and Kmeans and interactive clustering in Python
 
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Topic mining with LDA and Kmeans and interactive clustering in Python
Views: 315 OneLine News
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 74305 edureka!
Dunkirk - Trailer 1 [HD]
 
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From filmmaker Christopher Nolan (“Interstellar,” “Inception,” “The Dark Knight” Trilogy) comes the epic action thriller DUNKIRK, in theaters July 21, 2017. https://www.facebook.com/Dunkirkmovie/ https://twitter.com/dunkirkmovie http://instagram.com/dunkirkmovie/
Views: 40469487 Warner Bros. Pictures
Principal component analysis | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Principal_component_analysis 00:04:51 1 Intuition 00:06:21 2 Details 00:09:07 2.1 First component 00:12:44 2.2 Further components 00:12:56 2.3 Covariances 00:16:33 2.4 Dimensionality reduction 00:20:15 2.5 Singular value decomposition 00:20:29 3 Further considerations 00:24:56 4 Table of symbols and abbreviations 00:29:35 5 Properties and limitations of PCA 00:34:36 5.1 Properties 00:34:46 5.2 Limitations 00:34:57 5.3 PCA and information theory 00:41:41 6 Computing PCA using the covariance method 00:42:43 6.1 Organize the data set 00:45:59 6.2 Calculate the empirical mean 00:46:47 6.3 Calculate the deviations from the mean 00:47:30 6.4 Find the covariance matrix 00:48:16 6.5 Find the eigenvectors and eigenvalues of the covariance matrix 00:49:36 6.6 Rearrange the eigenvectors and eigenvalues 00:50:35 6.7 Compute the cumulative energy content for each eigenvector 00:52:53 6.8 Select a subset of the eigenvectors as basis vectors 00:53:16 6.9 Project the z-scores of the data onto the new basis 00:54:19 7 Derivation of PCA using the covariance method 00:55:54 8 Covariance-free computation 00:56:40 8.1 Iterative computation 00:58:45 8.2 The NIPALS method 00:59:27 8.3 Online/sequential estimation 01:03:25 9 PCA and qualitative variables 01:05:26 10 Applications 01:05:54 10.1 Quantitative finance 01:07:12 10.2 Neuroscience 01:07:21 11 Relation with other methods 01:08:31 11.1 Correspondence analysis 01:10:57 11.2 Factor analysis 01:11:07 11.3 iK/i-means clustering 01:12:06 11.4 Non-negative matrix factorization 01:13:58 12 Generalizations 01:14:37 12.1 Sparse PCA 01:16:37 12.2 Nonlinear PCA 01:16:47 12.3 Robust PCA 01:17:54 13 Similar techniques 01:19:37 13.1 Independent component analysis 01:20:46 13.2 Network component analysis 01:20:55 14 Software/source code 01:21:17 15 See also 01:23:05 16 References Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.9965622235318787 Voice name: en-AU-Wavenet-A "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. If there are n {\displaystyle n} observations with p {\displaystyle p} variables, then the number of distinct principal components is min ( n − 1 , p ) {\displaystyle \min(n-1,p)} . This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors (each being a linear combination of the variables and containing n observations) are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables. PCA was invented in 1901 by Karl Pearson, as an analogue of the principal axis theorem in mechanics; it was later independently developed and named by Harold Hotelling in the 1930s. Depending on the field of application, it is also named the discrete Karhunen–Loève transform (KLT) in signal processing, the Hotelling transform in multivariate quality control, proper orthogonal decomposition (POD) in mechanical engineering, singular value decomposition (SVD) of X (Golub and Van Loan, 1983), eigenvalue decomposition (EVD) of XTX in linear algebra, factor analysis (for a discussion of the differences between PCA and factor analysis see Ch. 7 of Jolliffe's Principal Component Analysis), Eckart ...
Views: 31 wikipedia tts
Algebraic Techniques for Multilingual Document Clustering
 
01:01:52
Google Tech Talks January 25, 2011 Presented by Brett Bader. ABSTRACT Multilingual documents pose difficulties for clustering by topic, not least because translating everything to a common language is not feasible with a large corpus or many languages. This presentation will address those difficulties with a variety of novel algebraic methods for efficiently clustering multilingual text documents, and brieflyillustrate their implementation via high performance computing. The methods use a multilingual parallel corpus as a 'Rosetta Stone' from which algorithmic variations (including statistical morphological analysis to bypass the need for stemming) of Latent Semantic Analysis (LSA) are able to learn concepts in term space. New documents are projected into this concept space to produce language-independent feature vectors for subsequent use in similarity calculations or machine learning applications. Our experiments show that the new methods have better performance than LSA, and possess some interesting and counter-intuitive properties. Brett W. Bader received his Ph.D. in computer science from the University of Colorado at Boulder, studying higher-order methods for optimization and solving systems of nonlinear equations. In 2003, Brett received the John von Neumann Research Fellowship at Sandia National Laboratories, where he now develops algorithms for multi-way data analysis and machine learning for informatics applications in networks and text.
Views: 3425 GoogleTechTalks
SVM with polynomial kernel visualization
 
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A visual demonstration of the kernel trick in SVM. This short video demonstrates how vectors of two classes that cannot be linearly separated in 2-D space, can become linearly separated by a transformation function into a higher dimensional space. The transformation used is: f([x y]) = [x y (x^2+y^2)] If you would like a stand-alone file with a high-res version of this movie for academic purposes please contact me. Visit my homepage http://www.zutopedia.com/udia.html, or read about my latest book "Zuto: The Adventures of a Computer Virus", http://www.zutopedia.com
Views: 293303 udiprod
What is the full form of RSVP
 
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All gherelu nushke are there, any medicine should to taken according to doctor prescription. It is totally up to you. My knowledge is based on my personal studies All the political views are my own views. My others Channels LINKS are : Shiv Hari Bhajan : https://www.youtube.com/channel/UCSCjzdEH7sZVWuvkg7ApLYQ Bhajan Baaje : https://www.youtube.com/channel/UCnsIAaPFcrUdOAtAJ3fokEA Harsh Infotainment : https://www.youtube.com/channel/UCEwDnh3NHYTjHuEA4eYisbA All gherelu nushke are there, any medicine should to taken according to doctor prescription. It is totally up to you. My knowledge is based on my personal studies All the political views are my own views. Shiv Hari Bhajan : https://www.youtube.com/channel/UCSCjzdEH7sZVWuvkg7ApLYQ Vastu & Astrology : https://www.youtube.com/channel/UCbhc-xEKXBrPHPevA2ibJxQ Ghar Ka Khana - https://www.youtube.com/channel/UCTnETM8SXLVChvnf6wAfyxg Bhajan Baaje : https://www.youtube.com/channel/UCnsIAaPFcrUdOAtAJ3fokEA Harsh Infotainment : https://www.youtube.com/channel/UCEwDnh3NHYTjHuEA4eYisbA RSVP - Répondez, S'il Vous Plaît (in French) which means "please reply." The term RSVP comes from the French expression which means the person sending the invitation would like you to tell him or her whether you accept or decline the invitation. It is used in a situation when somebody running some sort of a function, it gives a way that he or she can judge the expected number of guests that will attend the function. So, the next time you see RSVP on an invitation you receive, please call your host and respond promptly. More important, though, is the simple courtesy of responding to someone who was nice enough to invite you, even if it is to say that you regret that you will not be able to attend. Vastu & Astrology : https://www.youtube.com/channel/UCbhc-xEKXBrPHPevA2ibJxQ Dharm अाैर Science : https://www.youtube.com/channel/UC_7ylmMqDS92eLK6NY76Y6w Difference & Similarities : https://www.youtube.com/channel/UCTIp54Q_dyw1o2etqsoeXiw What If ….कया हो अगर : https://www.youtube.com/channel/UCnsIAaPFcrUdOAtAJ3fokEA Parenting & Lifestyle : https://www.youtube.com/channel/UChx9OYduRzQsdvyQBRIAq8A Shrimad Home Remedies : https://www.youtube.com/channel/UC3oHEjYN8K8eSH2LDq--5eQ Ghar Ka Khana : https://www.youtube.com/channel/UCTnETM8SXLVChvnf6wAfyxg Teej Tyohar - https://c.mp.ucweb.com/personal/index/58f59d88bae5476db984a49f3e5b5a8a?uc Guru Ka Gyan - https://c.mp.ucweb.com/personal/index/d4fd204f35014a458bb80deacbb4a925?uc Shreemad Home Remidies - https://c.mp.ucweb.com/personal/index/1ad6051206a446739f7e4fbf6f72035c?uc Ghar Ka Khana - https://c.mp.ucweb.com/personal/index/93f4321fdb504bec8e7ed24ae784b728?uc Teej Tyohar - https://c.mp.ucweb.com/personal/index/58f59d88bae5476db984a49f3e5b5a8a?uc Guru Ka Gyan - https://c.mp.ucweb.com/personal/index/d4fd204f35014a458bb80deacbb4a925?uc Shreemad Home Remidies - https://c.mp.ucweb.com/personal/index/1ad6051206a446739f7e4fbf6f72035c?uc Ghar Ka Khana - https://c.mp.ucweb.com/personal/index/93f4321fdb504bec8e7ed24ae784b728?uc All gherelu nushke are there, any medicine should to taken according to doctor prescription. It is totally up to you. My knowledge is based on my personal studies All the political views are my own views. Shiv Hari Bhajan : https://www.youtube.com/channel/UCSCjzdEH7sZVWuvkg7ApLYQ Vastu & Astrology : https://www.youtube.com/channel/UCbhc-xEKXBrPHPevA2ibJxQ Ghar Ka Khana - https://www.youtube.com/channel/UCTnETM8SXLVChvnf6wAfyxg Bhajan Baaje : https://www.youtube.com/channel/UCnsIAaPFcrUdOAtAJ3fokEA Harsh Infotainment : https://www.youtube.com/channel/UCEwDnh3NHYTjHuEA4eYisbA
Views: 77296 Gyan-The Treasure
Hadoop MapReduce Example | MapReduce Programming | Hadoop Tutorial For Beginners | Edureka
 
01:00:33
( Hadoop Training: https://www.edureka.co/hadoop ) This Hadoop tutorial on MapReduce Example ( Mapreduce Tutorial Blog Series: https://goo.gl/cZmvLS ) will help you understand how to write a MapReduce program in Java. You will also get to see multiple mapreduce examples on Analytics and Testing. Check our complete Hadoop playlist here: https://goo.gl/ExJdZs Below are the topics covered in this tutorial: 1) MapReduce Way 2) Classes and Packages in MapReduce 3) Explanation of a Complete MapReduce Program 4) MapReduce Examples on Analytics 5) MapReduce Example on Testing - MRUnit Subscribe to our channel to get video updates. Hit the subscribe button above. Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #edurekaMapreduce #MapReduceExample #MapReduceAnalytics #MapReduceTesting How it Works? 1. This is a 5 Week Instructor led Online Course, 40 hours of assignment and 30 hours of project work 2. 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. 3. 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 Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig, Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark - - - - - - - - - - - - - - Who should go for this course? If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building successful career in Big Data - - - - - - - - - - - - - - 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. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Customer Review: Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favorite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! Edureka lets you go back later, when your boss says "I want this ASAP!" ~ This is the killer education app... I've take two courses, and I'm taking two more.”
Views: 93157 edureka!
SCP-701 The Hanged King's Tragedy | Euclid class | Humanoid / performance scp
 
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SCP 701, The Hanged King's Tragedy, is a Caroline-era revenge tragedy in five acts. Performances of the play are associated with sudden psychotic and suicidal behavior among both observers and participants, as well as the manifestation of a mysterious figure, classified as SCP-701-1. Historical estimates place the number of lives claimed by the play at between █████ and █████ over the past three hundred years. Read along with me! ♣Read along: http://scp-wiki.wikidot.com/scp-701 http://scp-wiki.wikidot.com/scp7011640b1 http://scp-wiki.wikidot.com/incident-report-scp70119971 Help me out on Patreon! ▼Patreon▼ https://www.patreon.com/EastsideShow Join me on Facebook and Twitter! ♣Facebook: https://www.facebook.com/EastsideShow ♣Twitter: https://twitter.com/Eastsideshow "Long note One" Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/ Other ♣Music by Kevin MacLeod: http://incompetech.com/ ♥Be sure to like, comment, share, and subscribe!♥ #scp #scpfoundation #eastsideshow #creepypasta #eastsideshowscp
Views: 37431 The Eastside Show
LSI Jacker Software Demo Part 6 for Latent Semantic Indexing Rankings.
 
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LSI Jacker Demo 6 Latent Semantic Index Tutorial Latent Semantic Index Tutorial Part 1 for LSI Jacker Software. More Info at http://www.LSIJacker.com.
Views: 380 New Level Success
SCP-1139 The Broken Tongue | euclid class | Church of the Broken God / language SCP
 
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SCP-1139 The Broken Tongue - Upon application of electricity a direct current, the object affects all individuals within a given radius through unknown means. Any person within the radius of effect begins speaking and writing a new language, though they apparently believe they are speaking their native tongue. Subjects lose the ability to speak or comprehend any prior known language(s). Linguistic analysis indicates that the new languages are fully formed languages, but all attempts at translation have been met with complete failure. Attempts at translation continue. Subjects have proven incapable of learning or re-learning any real world language after exposure to SCP-1139. Class AA amnesiacs successfully counteract the effect, though subjects are thereafter of little use to the Foundation, and Class AA amnesiacs are not advised in future testing. See Experiment Log 1139-1. Read along with me! ♣Read along: http://www.scp-wiki.net/scp-1139 "Lost Frontier" Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/ Help me out on Patreon! ▼Patreon▼ https://www.patreon.com/EastsideShowSCP Join me on Facebook and Twitter! ♣Facebook: https://www.facebook.com/EastsideShowscp ♣Twitter: https://twitter.com/Eastsideshowscp Other ♣Music by Kevin MacLeod: http://incompetech.com/ ♥Be sure to like, comment, share, and subscribe!♥ #scp #eastsideshow #scpfoundation
Views: 49586 The Eastside Show
SCP-2161 Blank Space | Euclid class | computer / document / Virus / Self-replicating SCP
 
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SCP-2161-1 is a collection of approximately 85 million pages of self-replicating A4 paper, the majority of which are blank. A small proportion of pages contain letters, figures or other markings, suggesting that SCP-2161-1 originally formed a single text. Read along with me! ♣Read along: http://www.scp-wiki.net/scp-2161 Help me out on Patreon! ▼Patreon▼ https://www.patreon.com/EastsideShow Join me on Facebook and Twitter! ♣Facebook: https://www.facebook.com/EastsideShow ♣Twitter: https://twitter.com/Eastsideshow "Long note One" Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/ Other ♣Music by Kevin MacLeod: http://incompetech.com/ ♥Be sure to like, comment, share, and subscribe!♥ #scp #eastsideshowscp #eastsideshow #creepypasta #scpfoundation
Views: 14086 The Eastside Show
Sniper Ghost Warrior 3 All Weapons Showcase (Primary / Secondary / Sidearm)
 
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Sniper Ghost Warrior 3 All Weapons Showcase (Primary / Secondary / Sidearm) All gameplay recorded with - http://e.lga.to/360gametv This guide shows you all currently available Weapons in Sniper Ghost Warrior 3 as Showcase. Primary Weapons: 00:08 - 01 - Ballance S-AR Metal 00:39 - 02 - XM-2015 01:15 - 03 - Stronskiy 98 01:52 - 04 - Brezatelya 02:31 - 05 - Dragoon SVD 03:05 - 06 - Knight 110 03:40 - 07 - ES-25 04:24 - 08 - Vykop 05:01 - 09 - Archer T-80 05:36 - 10 - BMT 03 06:22 - 11 - Rook SS-97 06:51 - 12 - ACC 50 07:38 - 13 - Shipunov K96 08:11 - 14 - Turret M96 Secondary Weapons: 08:45 - 15 - Archer AR15 09:13 - 16 - AKA-47 09:38 - 17 - Herstal 10:04 - 18 - KT-R 10:30 - 19 - Galeforce Long 10:56 - 20 - FM-3000 UM 11:32 - 21 - OFM 500 12:11 - 22 - Giovanni M4 12:52 - 23 - Origin-12 13:30 - 24 - Takedown Recurve Bow Sidearms: 14:11 - 25 - M1984 Pistol 14:34 - 26 - M1984 Pistol Rail 14:57 - 27 - Garett M9 15:24 - 28 - Herrvalt 99 15:51 - 29 - Wagram 21 16:17 - 30 - Bull 686 16:47 - 31 - SLP .45 17:11 - 32 - SP M23 17:35 - 33 - MP-40 Grad 18:01 - 34 - Sawn-off Shotgun Sniper Ghost Warrior 3 Weapon Locations https://www.youtube.com/playlist?list=PLuGZAFj5iqHfUqC3CUXQfUVdUeacWiQbI Sniper Ghost Warrior 3 Weapon Skins https://www.youtube.com/playlist?list=PLuGZAFj5iqHcmE-ewQHiw5mufXtr13sYV Sniper Ghost Warrior 3 All Guides https://www.youtube.com/playlist?list=PLuGZAFj5iqHeeRu1y0vu4zMqZsle03EgW Support / Donate Paypal: http://bit.ly/1JySiRV Patreon: https://www.patreon.com/360gametv Visit my sites / partner Website: www.360gametv.com Partner: http://e.lga.to/360gametv Twitter: http://twitter.com/360GameTV Subscribe: http://www.youtube.com/subscription_center?add_user=360GameTV Achievements / Trophies: -
Views: 40766 360GameTV
Mod-01 Lec-26 NLP and IR: How NLP has used IR, Toward Latent Semantic
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 1753 nptelhrd
Lecture 1 | Machine Learning (Stanford)
 
01:08:40
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 2164696 Stanford
Machine learning | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Machine_learning 00:01:19 1 Overview of Machine Learning 00:02:36 1.1 Machine learning tasks 00:06:13 2 History and relationships to other fields 00:08:45 2.1 Relation to data mining 00:10:13 2.2 Relation to optimization 00:11:05 2.3 Relation to statistics 00:11:57 3 Theory 00:14:05 4 Approaches 00:14:14 4.1 Types of learning algorithms 00:14:34 4.1.1 Supervised and semi-supervised learning 00:16:27 4.1.2 Unsupervised learning 00:17:56 4.1.3 Reinforcement learning 00:19:03 4.2 Processes and techniques 00:19:23 4.2.1 Feature learning 00:22:13 4.2.2 Sparse dictionary learning 00:23:20 4.2.3 Anomaly detection 00:25:20 4.2.4 Decision trees 00:26:27 4.2.5 Association rules 00:31:10 4.3 Models 00:31:18 4.3.1 Artificial neural networks 00:34:09 4.3.2 Support vector machines 00:35:08 4.3.3 Bayesian networks 00:36:06 4.3.4 Genetic algorithms 00:36:45 5 Applications 00:38:08 6 Limitations 00:38:57 6.1 Bias 00:40:52 7 Model assessments 00:42:33 8 Ethics 00:44:19 9 Software 00:44:34 9.1 Free and open-source software 00:44:44 9.2 Proprietary software with free and open-source editions 00:44:56 9.3 Proprietary software 00:45:06 10 Journals 00:45:25 11 Conferences Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.9764280805268168 Voice name: en-US-Wavenet-B "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
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List of numerical analysis software | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/List_of_numerical_analysis_software 00:00:07 1 Numerical software packages 00:04:54 2 General-purpose computer algebra systems 00:07:09 3 Interface-oriented 00:10:24 4 Language-oriented 00:15:06 5 Historically significant 00:15:33 6 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.9991367021802131 Voice name: en-US-Wavenet-D "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Listed here are end-user computer applications intended for use with numerical or data analysis:
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Missionary | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Missionary 00:00:42 1 Missionaries by religion 00:00:51 1.1 Christian missions 00:01:37 1.1.1 Historic 00:08:53 1.1.2 Modern 00:13:56 1.1.2.1 Maryknoll 00:15:29 1.2 Islamic missions 00:23:22 1.2.1 Ahmadiyya Islam missions 00:24:30 1.2.2 Early Islamic missionaries during Muhammad's era 00:25:42 1.3 Missionaries and Judaism 00:27:12 1.4 Baha'i pioneering 00:27:20 1.5 Buddhist missions 00:34:42 1.6 Hindu missions 00:36:38 1.7 Sikh missions 00:39:31 1.8 Tenrikyo missions 00:39:56 1.9 Jain missions 00:42:43 1.10 Ananda Marga missions 00:44:05 2 Criticism 00:45:37 3 Impact of missions 00:47:30 4 Lists of prominent missionaries 00:47:40 4.1 American missionaries 00:49:13 4.2 British Christian missionaries 00:49:53 4.3 See also 00:50:53 5 See also 00:51:02 6 Notes 00:51:11 7 General references 00:52:26 8 External links Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= A missionary is a member of a religious group sent into an area to proselytize or perform ministries of service, such as education, literacy, social justice, health care, and economic development. The word "mission" originates from 1598 when the Jesuits sent members abroad, derived from the Latin missionem (nom. missio), meaning "act of sending" or mittere, meaning "to send". The word was used in light of its biblical usage; in the Latin translation of the Bible, Christ uses the word when sending the disciples to preach The gospel in his name. The term is most commonly used for Christian missions, but can be used for any creed or ideology.
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Legazpi, Albay | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Legazpi, Albay Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ In case you don't find one that you were looking for, put a comment. This video uses Google TTS en-US-Standard-D voice. SUMMARY ======= Legazpi, officially the City of Legazpi, (Central Bicolano: Ciudad kan Legazpi; Filipino: Lungsod ng Legazpi; Spanish: Ciudad de Legazpi ) and often referred to as Legazpi City, is a component city and the capital of the province of Albay in the Philippines. According to the 2015 census, it has a population of 196,639. Legazpi is the regional center and largest city of the Bicol Region, in terms of population. It is the region's center of tourism, education, health services, commerce and transportation in the Bicol Region. The city is composed of two districts: Legazpi Port and Old Albay District. Mayon Volcano, one of the Philippines' most popular icons and tourist destinations, is partly within the city's borders.In 2018, Legazpi was ranked first in overall competitiveness among component cities by the National Competitiveness Council. The city also ranked first in infrastructure and second in economic dynamism. In the same year, Legazpi was also named "most business-friendly city" in the component city category by the Philippine Chamber of Commerce and Industry.
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Jharkhand | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Jharkhand 00:00:55 1 History 00:04:59 1.1 British rule 00:07:02 1.2 Post-independence 00:08:03 1.3 Jharkhand statehood 00:09:04 1.4 Naxal insurgency 00:10:51 2 Geography 00:11:42 2.1 Climate 00:12:53 2.2 Hills and Mountain Ranges 00:14:56 2.3 Main Rivers 00:16:16 2.4 Flora and Fauna 00:17:08 3 Demographics 00:17:49 3.1 Languages 00:18:14 3.2 Religion 00:18:41 4 Government and administration 00:19:36 4.1 Administrative districts 00:20:12 4.2 Divisions and districts 00:20:21 4.3 Major cities 00:20:35 5 Economy 00:22:11 6 Culture 00:22:20 6.1 Cuisine 00:23:30 6.2 Folk Music and Dance 00:24:01 6.3 Festivals 00:24:22 6.4 Paintings 00:24:37 6.5 Tattoo 00:24:51 6.6 Cinema 00:25:09 7 Transport 00:25:17 7.1 Air 00:25:50 7.2 Roads 00:26:46 7.3 Ports 00:27:13 7.4 Rail 00:27:27 8 Education 00:30:04 8.1 Schools 00:31:03 8.2 Universities and colleges 00:31:52 8.2.1 Autonomous 00:32:38 8.2.2 Agriculture 00:32:51 8.2.3 Engineering 00:33:04 8.2.4 Management 00:33:16 8.2.5 Medical colleges 00:33:38 8.2.6 Psychiatry 00:33:48 9 Public Health 00:37:10 10 Sports 00:39:43 11 Tourism 00:40:08 12 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= Jharkhand (lit. "Bushland" or The land of forest) is a state in eastern India, carved out of the southern part of Bihar on 15 November 2000. The state shares its border with the states of Bihar to the north, Uttar Pradesh to the northwest, Chhattisgarh to the west, Odisha to the south and West Bengal to the east. It has an area of 79,710 km2 (30,778 sq mi). The city of Ranchi is its capital and Dumka its sub capital. Jharkhand suffers from resource curse; It accounts for more than 40% of the mineral resources of India, but it suffers widespread poverty as 39.1% of the population is below the poverty line and 19.6% of the children under five years of age are malnourished. The state is primarily rural, with only 24% of the population living in cities.
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