�ݺ�ߣshows by User: VenkateshVinayakarao

�ݺ�ߣshows by User: VenkateshVinayakarao / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: VenkateshVinayakarao / Wed, 11 Apr 2018 13:45:54 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: VenkateshVinayakarao Term Frequency and its Variants in Retrieval Models /slideshow/term-frequency-and-its-variants-in-retrieval-models/93555651 retrievalmodels-vv-teaser-11-4-2018-180411134554
A web search engine is concerned about mainly three things: 1) a query which is characterized by the user's information need, 2) a ranked list of relevant responses, and most importantly, 3) the magic that we call as a retrieval system which churns the content and returns the relevant responses. The beauty of such a retrieval system is hidden in its retrieval model. We start from a bare-bone vector space model and keep converting our intuitions and observations into mathematical models. We discuss the variants of IDF and TF-IDF models. As math meets Information Retrieval (IR) in this fast-paced and fun-filled talk, I hope to leave you with ideas on quickly converting these ideas into tools for your own area of research. I will also use this as a case to explain one way of "Ideating - Elaborating - Applying - Publishing" model of research. Some of my work which is currently under submission is masked in this presentation. Please wait till it gets published before approaching me for a full copy.]]>
A web search engine is concerned about mainly three things: 1) a query which is characterized by the user's information need, 2) a ranked list of relevant responses, and most importantly, 3) the magic that we call as a retrieval system which churns the content and returns the relevant responses. The beauty of such a retrieval system is hidden in its retrieval model. We start from a bare-bone vector space model and keep converting our intuitions and observations into mathematical models. We discuss the variants of IDF and TF-IDF models. As math meets Information Retrieval (IR) in this fast-paced and fun-filled talk, I hope to leave you with ideas on quickly converting these ideas into tools for your own area of research. I will also use this as a case to explain one way of "Ideating - Elaborating - Applying - Publishing" model of research. Some of my work which is currently under submission is masked in this presentation. Please wait till it gets published before approaching me for a full copy.]]> Wed, 11 Apr 2018 13:45:54 GMT /slideshow/term-frequency-and-its-variants-in-retrieval-models/93555651 VenkateshVinayakarao@slideshare.net(VenkateshVinayakarao) Term Frequency and its Variants in Retrieval Models VenkateshVinayakarao A web search engine is concerned about mainly three things: 1) a query which is characterized by the user's information need, 2) a ranked list of relevant responses, and most importantly, 3) the magic that we call as a retrieval system which churns the content and returns the relevant responses. The beauty of such a retrieval system is hidden in its retrieval model. We start from a bare-bone vector space model and keep converting our intuitions and observations into mathematical models. We discuss the variants of IDF and TF-IDF models. As math meets Information Retrieval (IR) in this fast-paced and fun-filled talk, I hope to leave you with ideas on quickly converting these ideas into tools for your own area of research. I will also use this as a case to explain one way of "Ideating - Elaborating - Applying - Publishing" model of research. Some of my work which is currently under submission is masked in this presentation. Please wait till it gets published before approaching me for a full copy. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/retrievalmodels-vv-teaser-11-4-2018-180411134554-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> A web search engine is concerned about mainly three things: 1) a query which is characterized by the user's information need, 2) a ranked list of relevant responses, and most importantly, 3) the magic that we call as a retrieval system which churns the content and returns the relevant responses. The beauty of such a retrieval system is hidden in its retrieval model. We start from a bare-bone vector space model and keep converting our intuitions and observations into mathematical models. We discuss the variants of IDF and TF-IDF models. As math meets Information Retrieval (IR) in this fast-paced and fun-filled talk, I hope to leave you with ideas on quickly converting these ideas into tools for your own area of research. I will also use this as a case to explain one way of "Ideating - Elaborating - Applying - Publishing" model of research. Some of my work which is currently under submission is masked in this presentation. Please wait till it gets published before approaching me for a full copy.

Term Frequency and its Variants in Retrieval Models from Venkatesh Vinayakarao

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0 Bayesian data analysis /slideshow/bayesian-data-analysis/41463652 bayesiandataanalysis-141112100931-conversion-gate02
It is hard to live without generating and using data. Bayesian Data Analysis builds upon basic probability theory and gives us the necessary tools to deal with realistic data. In this session, I will make no assumptions on your knowledge of probability or random processes. Using the very popular case of coin tosses, I introduce the concepts of Prior, Likelihood and Posteriors. This gives me the platform to discuss Bayes Rule and Bernoulli Distribution. Further, the algebraic limitation of such an analysis leads us to appreciate the purpose and properties of Beta Distributions. If you have heard of "Weak Priors", "Strong Priors" and "Conjugates", but never quite got a chance to study them, you will find this talk very interesting. Ever since my joining PhD@IIIT, it has been a journey through Randomized Algorithms, Information Retrieval, Intelligent Systems and Statistical Computation - all of which deal with related perspectives on probability theory. General wisdom claims that discussing fundamentals is relatively harder than discussing advanced stuff. I look forward for this discussion and hope that you will find value in it.]]>
It is hard to live without generating and using data. Bayesian Data Analysis builds upon basic probability theory and gives us the necessary tools to deal with realistic data. In this session, I will make no assumptions on your knowledge of probability or random processes. Using the very popular case of coin tosses, I introduce the concepts of Prior, Likelihood and Posteriors. This gives me the platform to discuss Bayes Rule and Bernoulli Distribution. Further, the algebraic limitation of such an analysis leads us to appreciate the purpose and properties of Beta Distributions. If you have heard of "Weak Priors", "Strong Priors" and "Conjugates", but never quite got a chance to study them, you will find this talk very interesting. Ever since my joining PhD@IIIT, it has been a journey through Randomized Algorithms, Information Retrieval, Intelligent Systems and Statistical Computation - all of which deal with related perspectives on probability theory. General wisdom claims that discussing fundamentals is relatively harder than discussing advanced stuff. I look forward for this discussion and hope that you will find value in it.]]> Wed, 12 Nov 2014 10:09:31 GMT /slideshow/bayesian-data-analysis/41463652 VenkateshVinayakarao@slideshare.net(VenkateshVinayakarao) Bayesian data analysis VenkateshVinayakarao It is hard to live without generating and using data. Bayesian Data Analysis builds upon basic probability theory and gives us the necessary tools to deal with realistic data. In this session, I will make no assumptions on your knowledge of probability or random processes. Using the very popular case of coin tosses, I introduce the concepts of Prior, Likelihood and Posteriors. This gives me the platform to discuss Bayes Rule and Bernoulli Distribution. Further, the algebraic limitation of such an analysis leads us to appreciate the purpose and properties of Beta Distributions. If you have heard of "Weak Priors", "Strong Priors" and "Conjugates", but never quite got a chance to study them, you will find this talk very interesting. Ever since my joining PhD@IIIT, it has been a journey through Randomized Algorithms, Information Retrieval, Intelligent Systems and Statistical Computation - all of which deal with related perspectives on probability theory. General wisdom claims that discussing fundamentals is relatively harder than discussing advanced stuff. I look forward for this discussion and hope that you will find value in it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bayesiandataanalysis-141112100931-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> It is hard to live without generating and using data. Bayesian Data Analysis builds upon basic probability theory and gives us the necessary tools to deal with realistic data. In this session, I will make no assumptions on your knowledge of probability or random processes. Using the very popular case of coin tosses, I introduce the concepts of Prior, Likelihood and Posteriors. This gives me the platform to discuss Bayes Rule and Bernoulli Distribution. Further, the algebraic limitation of such an analysis leads us to appreciate the purpose and properties of Beta Distributions. If you have heard of "Weak Priors", "Strong Priors" and "Conjugates", but never quite got a chance to study them, you will find this talk very interesting. Ever since my joining PhD@IIIT, it has been a journey through Randomized Algorithms, Information Retrieval, Intelligent Systems and Statistical Computation - all of which deal with related perspectives on probability theory. General wisdom claims that discussing fundamentals is relatively harder than discussing advanced stuff. I look forward for this discussion and hope that you will find value in it.

Bayesian data analysis from Venkatesh Vinayakarao

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0 Beauty ofir /slideshow/beauty-ofir/33256360 beautyofir-140408002740-phpapp02
Information Retrieval is about how we can search and retrieve things. In this talk, we look at the various components that make up a typical search engine and discuss the associated challenges.]]>
Information Retrieval is about how we can search and retrieve things. In this talk, we look at the various components that make up a typical search engine and discuss the associated challenges.]]> Tue, 08 Apr 2014 00:27:40 GMT /slideshow/beauty-ofir/33256360 VenkateshVinayakarao@slideshare.net(VenkateshVinayakarao) Beauty ofir VenkateshVinayakarao Information Retrieval is about how we can search and retrieve things. In this talk, we look at the various components that make up a typical search engine and discuss the associated challenges. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/beautyofir-140408002740-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Information Retrieval is about how we can search and retrieve things. In this talk, we look at the various components that make up a typical search engine and discuss the associated challenges.

Beauty ofir from Venkatesh Vinayakarao

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0 Ai swarm intelligence /VenkateshVinayakarao/ai-swarm-intelligence ai-swarmintelligence-140408001425-phpapp01
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.]]>
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.]]> Tue, 08 Apr 2014 00:14:25 GMT /VenkateshVinayakarao/ai-swarm-intelligence VenkateshVinayakarao@slideshare.net(VenkateshVinayakarao) Ai swarm intelligence VenkateshVinayakarao Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ai-swarmintelligence-140408001425-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.

Ai swarm intelligence from Venkatesh Vinayakarao

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0 https://cdn.slidesharecdn.com/profile-photo-VenkateshVinayakarao-48x48.jpg?cb=1529317855 vvtesh.co.in https://cdn.slidesharecdn.com/ss_thumbnails/retrievalmodels-vv-teaser-11-4-2018-180411134554-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/term-frequency-and-its-variants-in-retrieval-models/93555651 Term Frequency and its... https://cdn.slidesharecdn.com/ss_thumbnails/bayesiandataanalysis-141112100931-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/bayesian-data-analysis/41463652 Bayesian data analysis https://cdn.slidesharecdn.com/ss_thumbnails/beautyofir-140408002740-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/beauty-ofir/33256360 Beauty ofir