Pagerank
•Download as PPT, PDF•
0 likes•228 views
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Report
Share
Report
Share
![Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Economia
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank(2)
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
The Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Recommended
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Economia
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank(2)
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
The Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Resultadosenagas23102012
Enagás informó sobre sus resultados para los primeros nueve meses de 2012. Los ingresos totales aumentaron un 2,6% a 868,8 millones de euros, mientras que el EBITDA y el beneficio neto crecieron un 4,4% y un 3,9% respectivamente. Las inversiones fueron de 647,9 millones de euros, superando el objetivo anual. A pesar de un entorno económico difícil, Enagás continúa controlando costes para cumplir con sus objetivos a pesar de retrasos en integraciones.
Ayudas IDAE '09
Jornada sobre Eficiencia energética y gestión sostenible de residuos en la industria alimentaria
FIAB
Mayo '09
Ejercitando en power point macarena
Una webquest es una actividad de investigación guiada en la que los estudiantes interactúan con recursos de Internet para completar una tarea. Se identifica por su estructura, la cual incluye una introducción, descripción de la tarea, proceso para completarla, evaluación y conclusión. El tema debe ser relevante para los objetivos de la asignatura y permitir creatividad e investigación. La tarea guía el proceso y puede incluir compilación, análisis u otras actividades. El proceso describe los pasos para completar la tarea de forma ordenada.
7
nuestro trabajo de ELABORACION DE PAGINAS WEB
EQUIPO
LEONARDO BALDERAS LEDEZMA
JOSE DE JESUS COTA CUEVAS
ITZEL NATALY NAVARRO GONZALEZ
GUADALUPE LIZETH VASQUEZ
RUBI MARLENE LINARES REYNA
Suministro eléctrico
Breve repaso a sus derechos como
usuarios en el mercado eléctrico liberalizado, de manera que puedan
demandar en las mejores condiciones un suministro de calidad, a precios
claramente comparables, transparentes y razonables
Técnicas de Trabajo Cooperativo
El documento describe dos técnicas cooperativas: Co-Op Co-Op (Kagan), que involucra la organización de grupos heterogéneos de estudiantes para preparar y presentar subtemas de un tema principal, y Rompecabezas (Aronson et al.), que divide un tema en partes y asigna estudiantes expertos en cada parte para luego reunirse y enseñarse mutuamente. Ambas técnicas enfatizan la interdependencia positiva, interacción, responsabilidad individual y habilidades sociales.
Trabajo impactos ambientales 3º eso
El documento habla sobre varios temas relacionados con la contaminación del aire y el medio ambiente. Explica problemas como la lluvia ácida, la destrucción de la capa de ozono, y el efecto invernadero. También menciona la contaminación acústica y problemas con el agua como el consumo excesivo y la contaminación. Otras secciones cubren temas como la contaminación del suelo, la pérdida de biodiversidad, el ahorro energético, y los recursos naturales renovables y no renovables.
Contaminacion Bahia Quintero Puchuncavi
Este documento resume la situación medioambiental de la Bahía de Quintero en las comunas de Quintero y Puchuncaví. Detalla los principales usos de la bahía, incluyendo habitacional, pesquero, agrícola, turístico, industrial y portuario. También describe los antecedentes de contaminación, incluyendo contaminación del aire, terrestre, marina y de aguas continentales. Finalmente, cubre nuevos proyectos propuestos para la zona y sus posibles impactos negativos en la salud humana y los ecosistem
Pagerank
This document discusses the PageRank algorithm developed by Larry Page for ranking web pages. It introduces the motivation for developing PageRank to bring order to the unstructured web based on link analysis. It then describes the random surfer model that PageRank is based on and how it approximates page importance based on the quantity and quality of backlinks. The document concludes by discussing applications of PageRank and some issues with the algorithm, such as its inability to account for user intent in queries.
Page rank by university of michagain.ppt
1. PageRank is an algorithm developed by Larry Page and Sergey Brin to rate the importance of web pages by analyzing the link structure of the web. It assumes that more important pages are likely to receive more links from other pages.
2. PageRank models a user randomly clicking on links, getting bored, and randomly jumping to another page, with pages that are frequently visited in this random walk receiving a higher PageRank.
3. PageRank was a key part of the original Google search engine, which used it to help sort search results from most to least relevant. It remains an important algorithm for search engines today.
Ranking Web Pages
The document discusses the history and development of web search engines. It describes how early search engines in 1994 indexed around 100,000 pages while Google grew to index over 8 billion pages by 2005. It also explains the basic components and ranking algorithms of search engines, including PageRank, which calculates the importance of pages based on both the number and quality of inbound links.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines two Markov chains, one for hubs and one for authorities. PageRank is also based on random walks, and ranks pages highly if the sum of ranks of backlinking pages is high, accounting for both number of backlinks and importance of backlinking pages. Both algorithms bring order to the web by using link information to rank pages globally.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines hub and authority matrices to model the transitions between these types of pages. PageRank is also based on random walks, and ranks pages globally based on the number and importance of backlinks, as determined by the PageRank scores of the referring pages. It presents PageRank as modeling the probability distribution of a random surfer's transitions between pages. Both algorithms bring order to the web by using its graph structure and link-based relationships between pages.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines hub and authority matrices to model the transitions between these types of pages. PageRank is also based on random walks, and ranks pages highly if the sum of ranks of backlinking pages is high, modeling a random surfer. Both algorithms bring order to the web by using link structures, but SALSA focuses on specific communities while PageRank provides a global ranking.
Page Rank
PageRank is a method for ranking web pages based on the graph of the web. It takes advantage of the link structure to determine the importance or "rank" of each page. PageRank assumes that more important pages are likely to receive more links from other pages. It calculates a PageRank score through an iterative process that distributes weight from highly ranked pages to the pages they link to. The algorithm accounts for pages with no outgoing links and pages that link only to each other to avoid "rank sinks".
Pagerank (1)
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with one for hubs (pages that link to many others) and one for authorities (pages that many point to). It computes hub and authority scores for pages. PageRank also models the web as a graph, and computes page ranks based on the concept of a random surfer clicking on links. It factors in both the number of links pages have as well as the rank of pages that link to them. Both algorithms bring order to the web by using its link structure, but SALSA computes separate scores while PageRank computes a single rank value.
Pagerank
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with pages as either hubs or authorities. It computes hub and authority scores to find the principal community of related pages. PageRank also models the web as a graph, and defines a page's rank based on the ranks of pages that link to it, including a random surfer model to avoid rank sinks. Both leverage random walks to determine important pages based on the web's link structure.
Pagerank
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with one for hubs (pages with many outlinks) and one for authorities (pages with many inlinks). It computes hub and authority scores for pages based on the principal eigenvectors of these chains. PageRank also models the web as a graph, and computes page ranks based on a random walk model where a user randomly clicks on links. It assigns higher rank to pages linked from many highly ranked pages. Both use the concept of a random walk through the link graph to determine important pages.
Pagerank
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two interacting Markov chains, with one chain representing hubs (pages that link to many others) and the other representing authorities (pages that many point to). The principal community identified by SALSA consists of pages frequently visited in these random walks. PageRank also models the web as a graph and web surfing as a random walk. It defines a page's rank based on the ranks of pages that link to it, so pages linked from many highly ranked pages receive higher ranks. Both use random walk-based models to analyze the web's link structure and determine authoritative pages.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
More Related Content
Viewers also liked
Resultadosenagas23102012
Enagás informó sobre sus resultados para los primeros nueve meses de 2012. Los ingresos totales aumentaron un 2,6% a 868,8 millones de euros, mientras que el EBITDA y el beneficio neto crecieron un 4,4% y un 3,9% respectivamente. Las inversiones fueron de 647,9 millones de euros, superando el objetivo anual. A pesar de un entorno económico difícil, Enagás continúa controlando costes para cumplir con sus objetivos a pesar de retrasos en integraciones.
Ayudas IDAE '09
Jornada sobre Eficiencia energética y gestión sostenible de residuos en la industria alimentaria
FIAB
Mayo '09
Ejercitando en power point macarena
Una webquest es una actividad de investigación guiada en la que los estudiantes interactúan con recursos de Internet para completar una tarea. Se identifica por su estructura, la cual incluye una introducción, descripción de la tarea, proceso para completarla, evaluación y conclusión. El tema debe ser relevante para los objetivos de la asignatura y permitir creatividad e investigación. La tarea guía el proceso y puede incluir compilación, análisis u otras actividades. El proceso describe los pasos para completar la tarea de forma ordenada.
7
nuestro trabajo de ELABORACION DE PAGINAS WEB
EQUIPO
LEONARDO BALDERAS LEDEZMA
JOSE DE JESUS COTA CUEVAS
ITZEL NATALY NAVARRO GONZALEZ
GUADALUPE LIZETH VASQUEZ
RUBI MARLENE LINARES REYNA
Suministro eléctrico
Breve repaso a sus derechos como
usuarios en el mercado eléctrico liberalizado, de manera que puedan
demandar en las mejores condiciones un suministro de calidad, a precios
claramente comparables, transparentes y razonables
Técnicas de Trabajo Cooperativo
El documento describe dos técnicas cooperativas: Co-Op Co-Op (Kagan), que involucra la organización de grupos heterogéneos de estudiantes para preparar y presentar subtemas de un tema principal, y Rompecabezas (Aronson et al.), que divide un tema en partes y asigna estudiantes expertos en cada parte para luego reunirse y enseñarse mutuamente. Ambas técnicas enfatizan la interdependencia positiva, interacción, responsabilidad individual y habilidades sociales.
Trabajo impactos ambientales 3º eso
El documento habla sobre varios temas relacionados con la contaminación del aire y el medio ambiente. Explica problemas como la lluvia ácida, la destrucción de la capa de ozono, y el efecto invernadero. También menciona la contaminación acústica y problemas con el agua como el consumo excesivo y la contaminación. Otras secciones cubren temas como la contaminación del suelo, la pérdida de biodiversidad, el ahorro energético, y los recursos naturales renovables y no renovables.
Contaminacion Bahia Quintero Puchuncavi
Este documento resume la situación medioambiental de la Bahía de Quintero en las comunas de Quintero y Puchuncaví. Detalla los principales usos de la bahía, incluyendo habitacional, pesquero, agrícola, turístico, industrial y portuario. También describe los antecedentes de contaminación, incluyendo contaminación del aire, terrestre, marina y de aguas continentales. Finalmente, cubre nuevos proyectos propuestos para la zona y sus posibles impactos negativos en la salud humana y los ecosistem
Viewers also liked (8)
Similar to Pagerank
Pagerank
This document discusses the PageRank algorithm developed by Larry Page for ranking web pages. It introduces the motivation for developing PageRank to bring order to the unstructured web based on link analysis. It then describes the random surfer model that PageRank is based on and how it approximates page importance based on the quantity and quality of backlinks. The document concludes by discussing applications of PageRank and some issues with the algorithm, such as its inability to account for user intent in queries.
Page rank by university of michagain.ppt
1. PageRank is an algorithm developed by Larry Page and Sergey Brin to rate the importance of web pages by analyzing the link structure of the web. It assumes that more important pages are likely to receive more links from other pages.
2. PageRank models a user randomly clicking on links, getting bored, and randomly jumping to another page, with pages that are frequently visited in this random walk receiving a higher PageRank.
3. PageRank was a key part of the original Google search engine, which used it to help sort search results from most to least relevant. It remains an important algorithm for search engines today.
Ranking Web Pages
The document discusses the history and development of web search engines. It describes how early search engines in 1994 indexed around 100,000 pages while Google grew to index over 8 billion pages by 2005. It also explains the basic components and ranking algorithms of search engines, including PageRank, which calculates the importance of pages based on both the number and quality of inbound links.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines two Markov chains, one for hubs and one for authorities. PageRank is also based on random walks, and ranks pages highly if the sum of ranks of backlinking pages is high, accounting for both number of backlinks and importance of backlinking pages. Both algorithms bring order to the web by using link information to rank pages globally.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines hub and authority matrices to model the transitions between these types of pages. PageRank is also based on random walks, and ranks pages globally based on the number and importance of backlinks, as determined by the PageRank scores of the referring pages. It presents PageRank as modeling the probability distribution of a random surfer's transitions between pages. Both algorithms bring order to the web by using its graph structure and link-based relationships between pages.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines hub and authority matrices to model the transitions between these types of pages. PageRank is also based on random walks, and ranks pages highly if the sum of ranks of backlinking pages is high, modeling a random surfer. Both algorithms bring order to the web by using link structures, but SALSA focuses on specific communities while PageRank provides a global ranking.
Page Rank
PageRank is a method for ranking web pages based on the graph of the web. It takes advantage of the link structure to determine the importance or "rank" of each page. PageRank assumes that more important pages are likely to receive more links from other pages. It calculates a PageRank score through an iterative process that distributes weight from highly ranked pages to the pages they link to. The algorithm accounts for pages with no outgoing links and pages that link only to each other to avoid "rank sinks".
Pagerank (1)
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with one for hubs (pages that link to many others) and one for authorities (pages that many point to). It computes hub and authority scores for pages. PageRank also models the web as a graph, and computes page ranks based on the concept of a random surfer clicking on links. It factors in both the number of links pages have as well as the rank of pages that link to them. Both algorithms bring order to the web by using its link structure, but SALSA computes separate scores while PageRank computes a single rank value.
Pagerank
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with pages as either hubs or authorities. It computes hub and authority scores to find the principal community of related pages. PageRank also models the web as a graph, and defines a page's rank based on the ranks of pages that link to it, including a random surfer model to avoid rank sinks. Both leverage random walks to determine important pages based on the web's link structure.
Pagerank
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with one for hubs (pages with many outlinks) and one for authorities (pages with many inlinks). It computes hub and authority scores for pages based on the principal eigenvectors of these chains. PageRank also models the web as a graph, and computes page ranks based on a random walk model where a user randomly clicks on links. It assigns higher rank to pages linked from many highly ranked pages. Both use the concept of a random walk through the link graph to determine important pages.
Pagerank
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two interacting Markov chains, with one chain representing hubs (pages that link to many others) and the other representing authorities (pages that many point to). The principal community identified by SALSA consists of pages frequently visited in these random walks. PageRank also models the web as a graph and web surfing as a random walk. It defines a page's rank based on the ranks of pages that link to it, so pages linked from many highly ranked pages receive higher ranks. Both use random walk-based models to analyze the web's link structure and determine authoritative pages.
Similar to Pagerank (11)
Recently uploaded
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
SAP S/4 HANA sourcing and procurement to Public cloud
SAP S4 HANA to Public cloud data object differences
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
dbms calicut university B. sc Cs 4th sem.pdf
Its a seminar ppt on database management system using sql
Azure API Management to expose backend services securely
How to use Azure API Management to expose backend service securely
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Recently uploaded (20)
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
SAP S/4 HANA sourcing and procurement to Public cloud
SAP S/4 HANA sourcing and procurement to Public cloud
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Azure API Management to expose backend services securely
Azure API Management to expose backend services securely
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Pagerank
- 1. The PageRank Citation Ranking: Bringing Order to the Web Larry Page etc. Stanford University Presented by Guoqiang Su & Wei Li
- 20. Evaluation I
- 21. Evaluation II