A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications.
Storytelling with data think broad, mine deep, explain simplyLuciano Pesci, PhD
This is a presentation from the 2016 SLC|SEM Digital Marketing Conference in Salt Lake City August on 25th 2016. It uses Epic Rap Battles of History, the most successful internet show ever, as an example of how to tell a story with data analytics by thinking broadly, mining deeply, and explaining simply.
This document discusses recommender systems and collaborative filtering. It defines recommender systems as tools that help users make decisions by recommending items based on their preferences or the preferences of similar users. It describes two main types of recommender systems: content-based systems, which recommend items similar to those a user liked in the past, and collaborative filtering systems, which recommend items liked by other users with similar tastes. The document uses the example of Amazon and MovieLens to illustrate how collaborative filtering works by finding relationships between users or items in a user-item rating matrix.
The presentation used at the July 2011 Limestone New Media Group meetup - "Social Media 101"! A lot of great discussions and conversations arose from the slideshow, so I hope you'll be able to make it out to our future meetups.
ux academy - Beginner UX Design Course Portfolio - Louise MobileUXLondon
Users struggle to find all their favorite artists' albums across different music streaming services. The team interviewed music streaming service users to understand their pains and needs. They found that nearly 30% use Spotify primarily but users wish for better music discovery features across services, like integrating Shazam to save songs for later on Spotify. The team created a prototype to address these issues and allow unified music streaming, but need further iteration focused on new music, genres, and additional user testing.
This presentation shows how to use social media data and analysis in academic research in the field of marketing, economics, consumer behavior, and psychology. LiveWall (www.li.vewall.com) collects social data and allows you to use it for research.
This document provides an overview of a movie recommendation system project. It discusses using a movie dataset containing 5000 movies from TMDB. The project will use libraries like NumPy, Pandas, and Streamlit to preprocess the data, create a model for movie recommendations, and deploy the model through a Streamlit application. The scope of the project is to build a recommendation system that can predict movie ratings and provide personalized movie suggestions to users. The problem statement is to recommend movies to users based on their previous ratings and integrate social media analysis to improve recommendations.
Pubcon Vegas 2010 - Social Media: Measurements & ToolsAdam Proehl
This document discusses various social media measurement tools and metrics. It begins with an overview of common metrics like mentions, sentiment, share of voice, influencers, velocity, reach and share metrics. It then discusses limitations of social media dashboards and similarities to web analytics. The main section recommends focusing on actionable metrics that provide insights rather than just numbers. It emphasizes understanding context and motivations rather than just volume of actions. The document concludes by providing examples of free social media measurement and analytics tools.
Recommender systems aim to suggest relevant items to users based on their preferences and behaviors. They can be used on e-commerce sites to increase sales by recommending other products customers might like. Recommender systems operate by collecting data on users' purchases and searches to find patterns and recommend similar items. However, they face challenges around data quality, algorithm accuracy, and privacy controls. Future improvements may include better integrating user communities and predicting demand to communicate with supply chains earlier.
Storytelling with data think broad, mine deep, explain simplyLuciano Pesci, PhD
This is a presentation from the 2016 SLC|SEM Digital Marketing Conference in Salt Lake City August on 25th 2016. It uses Epic Rap Battles of History, the most successful internet show ever, as an example of how to tell a story with data analytics by thinking broadly, mining deeply, and explaining simply.
This document discusses recommender systems and collaborative filtering. It defines recommender systems as tools that help users make decisions by recommending items based on their preferences or the preferences of similar users. It describes two main types of recommender systems: content-based systems, which recommend items similar to those a user liked in the past, and collaborative filtering systems, which recommend items liked by other users with similar tastes. The document uses the example of Amazon and MovieLens to illustrate how collaborative filtering works by finding relationships between users or items in a user-item rating matrix.
The presentation used at the July 2011 Limestone New Media Group meetup - "Social Media 101"! A lot of great discussions and conversations arose from the slideshow, so I hope you'll be able to make it out to our future meetups.
ux academy - Beginner UX Design Course Portfolio - Louise MobileUXLondon
Users struggle to find all their favorite artists' albums across different music streaming services. The team interviewed music streaming service users to understand their pains and needs. They found that nearly 30% use Spotify primarily but users wish for better music discovery features across services, like integrating Shazam to save songs for later on Spotify. The team created a prototype to address these issues and allow unified music streaming, but need further iteration focused on new music, genres, and additional user testing.
This presentation shows how to use social media data and analysis in academic research in the field of marketing, economics, consumer behavior, and psychology. LiveWall (www.li.vewall.com) collects social data and allows you to use it for research.
This document provides an overview of a movie recommendation system project. It discusses using a movie dataset containing 5000 movies from TMDB. The project will use libraries like NumPy, Pandas, and Streamlit to preprocess the data, create a model for movie recommendations, and deploy the model through a Streamlit application. The scope of the project is to build a recommendation system that can predict movie ratings and provide personalized movie suggestions to users. The problem statement is to recommend movies to users based on their previous ratings and integrate social media analysis to improve recommendations.
Pubcon Vegas 2010 - Social Media: Measurements & ToolsAdam Proehl
This document discusses various social media measurement tools and metrics. It begins with an overview of common metrics like mentions, sentiment, share of voice, influencers, velocity, reach and share metrics. It then discusses limitations of social media dashboards and similarities to web analytics. The main section recommends focusing on actionable metrics that provide insights rather than just numbers. It emphasizes understanding context and motivations rather than just volume of actions. The document concludes by providing examples of free social media measurement and analytics tools.
Recommender systems aim to suggest relevant items to users based on their preferences and behaviors. They can be used on e-commerce sites to increase sales by recommending other products customers might like. Recommender systems operate by collecting data on users' purchases and searches to find patterns and recommend similar items. However, they face challenges around data quality, algorithm accuracy, and privacy controls. Future improvements may include better integrating user communities and predicting demand to communicate with supply chains earlier.
Just like a list, a tuple is also an ordered collection of Python objects. The only difference between a tuple and a list is that tuples are immutable i.e. tuples cannot be modified after it is created. It is represented by a tuple class
Recommender systems aim to suggest relevant items to users based on their preferences and behaviors. They automate word-of-mouth recommendations by analyzing patterns in user data. Common types include collaborative filtering, which identifies similar users' preferences, and content-based filtering, which uses machine learning to classify items. Recommender systems are widely used on e-commerce sites like Amazon to increase sales by matching users with new products. However, they also face challenges around data quality, algorithm accuracy, and user privacy. As more data becomes available, future recommender systems may better predict demand and integrate social community opinions.
Recommender systems aim to suggest relevant items to users based on their preferences and behaviors. They can be used on e-commerce sites to increase sales by recommending other products customers might like. Recommender systems operate by collecting data on users' purchases and searches to find patterns and recommend similar items. However, they face challenges around data quality, algorithm accuracy, and privacy controls. Future improvements may include better integrating user communities and predicting demand to communicate with supply chains earlier.
Recommender systems are a technological proxy for social recommendations that suggest similar items or ideas to users based on their tastes. They aim to automate word-of-mouth recommendations by analyzing user preferences and behavior patterns to suggest new content. Recommender systems are commonly used on e-commerce sites to improve sales by suggesting additional items customers may like. They can use collaborative filtering by comparing users, content-based filtering by analyzing item content, or knowledge-based approaches. Maintaining accurate recommender systems over time with large amounts of user data presents challenges.
This document provides an introduction to recommender systems. It discusses how recommender systems can help users filter through large amounts of information and options in an era of information overload. It describes different types of recommender systems, including content-based, collaborative filtering, and context-based recommender systems. The document also discusses challenges like sparsity in data and scaling to large datasets, and how modeling approaches can help address these challenges.
This document discusses using social media data in academic research. It provides an overview of the LiveWall platform, which aggregates data from social media sources like Twitter, Facebook, and Instagram. It then discusses the large volume of content and interactions on social media platforms. The document outlines different types of data that can be accessed from social media APIs and examples of how this data could be used for various research studies in fields like communication, marketing, economics, and psychology.
Recommender systems are software agents that analyze a user's preferences through transactions and provide personalized recommendations accordingly. There are several recommendation paradigms including non-personalized rules, personalized rules based on user data, and transaction-based collaborative filtering that learns from user interactions. Context-based recommender systems also consider additional information like time, location, or device to provide adaptive recommendations. Common techniques used in recommender systems include content-based filtering that recommends similar items, collaborative filtering that finds users with similar tastes, and demographic-based recommendations.
Recommender systems provide personalized recommendations to help users discover new items from a large selection. They are used by Amazon to suggest new products, Netflix to recommend movies, and Last.FM to suggest new music. The talk discusses how recommender systems work, including collaborative filtering based on user preferences and item similarities. It also covers challenges like dealing with sparse data and presenting recommendations to users without creating filter bubbles. The goal is to help users discover new items from the "long tail" of less popular options.
Get the most effective research strategies for communications professionals. The key topics covered include:
How to define and select the right audience
Identifying the pitfalls to avoid
Making the most of digital platforms and other tools
How to develop a strategy with the support of statistics
Practical methods which you can implement immediately
Knowing Your Audience: Communications Research Masterclass Enesha Nash, MBA
This document provides an overview of how to get to know your audience through internal and external research. It discusses defining your audience and personas, questioning them to understand their needs and interests, researching them through internal reports, interviews, and external sources like social data and media trends. The toolkit section lists various tools that can help with this research. Creating personas involves synthesizing this research into fictional character profiles. Finally, the document advises developing a strategy centered around your personas' needs, using channels and topics they engage with to generate new leads and meaningful relationships.
The document discusses leveraging social media for nonprofit fundraising success. It begins by defining social media as Internet-based tools for sharing information among people, primarily through user-generated content like words, pictures, audio and video. It then discusses how social media is really about powering conversations among people. It provides tips for nonprofits on engaging in these conversations by listening to supporters' interests, participating in discussions, sharing compelling content, generating buzz about their cause, and building communities through social networking. The key is for nonprofits to have conversations with supporters rather than just broadcast messages and to be willing to give up some control of the conversation.
Recommendation Systems - Why How and Real Life ApplicationsLiron Zighelnic
These slides were created for a presentation at MIT - Massachusetts Institution of Technology
- Data Analytics Club
Recommendations become very popular in almost every field of our lives, from movies, to news to dating. Many systems try to give us personal recommendations.
In this presentation we will examine:
- Why recommendations are important?
- What are the main methods and algorithms being used?
- Real life applications & who use it? (the question should be: who doesn’t?)
About CurtainApp:
CurtainApp is an intelligent mobile app that learns your taste and gives you personal fashion recommendations, making shopping fun and efficient
Visit: www.curtainapp.com
Join us on Facebook: facebook.com/CurtainApp
Follow us on Twitter: twitter.com/thecurtainapp
#MIT #mobileapp #recommendation #fashion #recommendersystems #paradoxofchoice #Google #Netflix #OkCupid #Pandora #Curtain
Eventbrite talk at SXSW interactive 2013. The talk is about recommendation systems. The talk goes in details of what, why, how and future of recommendation systems.
Sharing the Loves: Understanding the How and Why of Online Content CurationChangtao Zhong
ICWSM13 Paper Author:
Zhong, Changtao, Sunil Shah, Karthik Sundaravadivelan, and Nishanth Sastry.
Full paper available at: http://www.inf.kcl.ac.uk/staff/nrs/pubs/icwsm13.pdf
The Hive Think Tank: Machine Learning at Pinterest by Jure LeskovecThe Hive
Machine learning is at the core of Pinterest. Pinterest personalizes and ranks 1B+ pins, 700+ million boards for 100M+ users all over the world, using data gathered from collaborative filtering, user curation, web crawling, and more. At Pinterest we model relationships between pins, handle cold-start problems and deal with real-time recommendations.
In this presentation Jure gave an overview of the problems and effective solutions developed at Pinterest. He focused on systems and effective engineering choices made to enable productive machine learning development and enable multiple engineers effectively develop, test, and deploy machine-learned models.
Deezer - Big data as a streaming serviceJulie Knibbe
40 million songs, albums and artists available - how nice? Streaming allows you to get a grasp at the biggest music collections in the world. The only thing is that you would need centuries to listen to all of it.
Getting access doesn’t mean knowing what to do with it. How are we making music discovery more & more efficient at Deezer?
The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using “collective wisdom” distilled from actions of those who worked with this information earlier.
Social information access can be formally defined as a stream of research that explores methods for organizing users' past interaction with an information system (known as explicit and implicit feedback), in order to provide better access to information to the future users of the system. It covers a range of rather different systems and technologies from social navigation to collaborative filtering. An important feature of all social information access systems is self-organization. Social information access systems are able to work with little or no involvement of human indexers, organizers, or other kinds of experts. They are truly powered by a community of users. Due to this feature, social information access technologies are frequently considered as an alternative to the traditional (content-oriented) technologies. The goal of this tutorial is to provide an overview of the emerging social information access research stream and to provide some practical guidelines for building social information access systems.
Congratulations, you have an online community! Odds are, you also have an offline community. Are you using one to strengthen the other?
Most of the organizations I work with in my practice already have all the ingredients in place for a real, vibrant community that lives on and off line. Too often though, on- and offline are treated as separate worlds, with little effort made to bridge the gap. Communities thrive when there is varied and ongoing interaction. Merging physical and non-physical conversations, events, and activities is one of the strongest tactics for building community in the real world.
In this session, we'll talk about how communities form, the ingredients for engagement, the importance of culture, and tactics for bridging the gap.
Takeaways:
- An understanding of the different types and benefits of online and offline communities
- Tactics to kickstart their online and offline communities
- Ways to engage their communities both online and offline
This document provides guidance on conducting online research and evaluating sources. It discusses searching academic databases and filtering search engine results to find reliable information. The document outlines criteria for assessing source credibility, such as considering the author's credentials, publication date, purpose, and references. Students are encouraged to apply the CRAAP test to quickly evaluate whether a source is current, relevant, authoritative, accurate, and objective. The document also contains an in-class activity asking students to find sources on violence in video games, cite them, summarize one, and evaluate its usefulness for an argument on that topic.
Abstract:
Extensive research has been done in prefetching techniques that hide memory latency in microprocessors leading to performance improvements. However, the energy aspect of prefetching is relatively unknown. While aggressive prefetching techniques often help to improve performance, they increase energy consumption by as much as 30% in the memory system. This paper provides a detailed evaluation on the energy impact of hardware data prefetching and then presents a set of new energy-aware techniques to overcome prefetching energy overhead of such schemes. These include compiler-assisted and hardware-based energy-aware techniques and a new power-aware prefetch engine that can reduce hardware prefetching related energy consumption by 7-11 ×. Combined with the effect of leakage energy reduction due to performance improvement, the total energy consumption for the memory system after the application of these techniques can be up to 12% less than the baseline with no prefetching.
operating system calls input and output by (rohit malav)Rohit malav
Introduction of System Call
In computing, a system call is the programmatic way in which a computer program requests a service from the kernel of the operating system it is executed on. A system call is a way for programs to interact with the operating system. A computer program makes a system call when it makes a request to the operating system’s kernel. System call provides the services of the operating system to the user programs via Application Program Interface(API). It provides an interface between a process and operating system to allow user-level processes to request services of the operating system. System calls are the only entry points into the kernel system. All programs needing resources must use system calls.
Services Provided by System Calls :
Process creation and management
Main memory management
File Access, Directory and File system management
Device handling(I/O)
Protection
Networking, etc.
Just like a list, a tuple is also an ordered collection of Python objects. The only difference between a tuple and a list is that tuples are immutable i.e. tuples cannot be modified after it is created. It is represented by a tuple class
Recommender systems aim to suggest relevant items to users based on their preferences and behaviors. They automate word-of-mouth recommendations by analyzing patterns in user data. Common types include collaborative filtering, which identifies similar users' preferences, and content-based filtering, which uses machine learning to classify items. Recommender systems are widely used on e-commerce sites like Amazon to increase sales by matching users with new products. However, they also face challenges around data quality, algorithm accuracy, and user privacy. As more data becomes available, future recommender systems may better predict demand and integrate social community opinions.
Recommender systems aim to suggest relevant items to users based on their preferences and behaviors. They can be used on e-commerce sites to increase sales by recommending other products customers might like. Recommender systems operate by collecting data on users' purchases and searches to find patterns and recommend similar items. However, they face challenges around data quality, algorithm accuracy, and privacy controls. Future improvements may include better integrating user communities and predicting demand to communicate with supply chains earlier.
Recommender systems are a technological proxy for social recommendations that suggest similar items or ideas to users based on their tastes. They aim to automate word-of-mouth recommendations by analyzing user preferences and behavior patterns to suggest new content. Recommender systems are commonly used on e-commerce sites to improve sales by suggesting additional items customers may like. They can use collaborative filtering by comparing users, content-based filtering by analyzing item content, or knowledge-based approaches. Maintaining accurate recommender systems over time with large amounts of user data presents challenges.
This document provides an introduction to recommender systems. It discusses how recommender systems can help users filter through large amounts of information and options in an era of information overload. It describes different types of recommender systems, including content-based, collaborative filtering, and context-based recommender systems. The document also discusses challenges like sparsity in data and scaling to large datasets, and how modeling approaches can help address these challenges.
This document discusses using social media data in academic research. It provides an overview of the LiveWall platform, which aggregates data from social media sources like Twitter, Facebook, and Instagram. It then discusses the large volume of content and interactions on social media platforms. The document outlines different types of data that can be accessed from social media APIs and examples of how this data could be used for various research studies in fields like communication, marketing, economics, and psychology.
Recommender systems are software agents that analyze a user's preferences through transactions and provide personalized recommendations accordingly. There are several recommendation paradigms including non-personalized rules, personalized rules based on user data, and transaction-based collaborative filtering that learns from user interactions. Context-based recommender systems also consider additional information like time, location, or device to provide adaptive recommendations. Common techniques used in recommender systems include content-based filtering that recommends similar items, collaborative filtering that finds users with similar tastes, and demographic-based recommendations.
Recommender systems provide personalized recommendations to help users discover new items from a large selection. They are used by Amazon to suggest new products, Netflix to recommend movies, and Last.FM to suggest new music. The talk discusses how recommender systems work, including collaborative filtering based on user preferences and item similarities. It also covers challenges like dealing with sparse data and presenting recommendations to users without creating filter bubbles. The goal is to help users discover new items from the "long tail" of less popular options.
Get the most effective research strategies for communications professionals. The key topics covered include:
How to define and select the right audience
Identifying the pitfalls to avoid
Making the most of digital platforms and other tools
How to develop a strategy with the support of statistics
Practical methods which you can implement immediately
Knowing Your Audience: Communications Research Masterclass Enesha Nash, MBA
This document provides an overview of how to get to know your audience through internal and external research. It discusses defining your audience and personas, questioning them to understand their needs and interests, researching them through internal reports, interviews, and external sources like social data and media trends. The toolkit section lists various tools that can help with this research. Creating personas involves synthesizing this research into fictional character profiles. Finally, the document advises developing a strategy centered around your personas' needs, using channels and topics they engage with to generate new leads and meaningful relationships.
The document discusses leveraging social media for nonprofit fundraising success. It begins by defining social media as Internet-based tools for sharing information among people, primarily through user-generated content like words, pictures, audio and video. It then discusses how social media is really about powering conversations among people. It provides tips for nonprofits on engaging in these conversations by listening to supporters' interests, participating in discussions, sharing compelling content, generating buzz about their cause, and building communities through social networking. The key is for nonprofits to have conversations with supporters rather than just broadcast messages and to be willing to give up some control of the conversation.
Recommendation Systems - Why How and Real Life ApplicationsLiron Zighelnic
These slides were created for a presentation at MIT - Massachusetts Institution of Technology
- Data Analytics Club
Recommendations become very popular in almost every field of our lives, from movies, to news to dating. Many systems try to give us personal recommendations.
In this presentation we will examine:
- Why recommendations are important?
- What are the main methods and algorithms being used?
- Real life applications & who use it? (the question should be: who doesn’t?)
About CurtainApp:
CurtainApp is an intelligent mobile app that learns your taste and gives you personal fashion recommendations, making shopping fun and efficient
Visit: www.curtainapp.com
Join us on Facebook: facebook.com/CurtainApp
Follow us on Twitter: twitter.com/thecurtainapp
#MIT #mobileapp #recommendation #fashion #recommendersystems #paradoxofchoice #Google #Netflix #OkCupid #Pandora #Curtain
Eventbrite talk at SXSW interactive 2013. The talk is about recommendation systems. The talk goes in details of what, why, how and future of recommendation systems.
Sharing the Loves: Understanding the How and Why of Online Content CurationChangtao Zhong
ICWSM13 Paper Author:
Zhong, Changtao, Sunil Shah, Karthik Sundaravadivelan, and Nishanth Sastry.
Full paper available at: http://www.inf.kcl.ac.uk/staff/nrs/pubs/icwsm13.pdf
The Hive Think Tank: Machine Learning at Pinterest by Jure LeskovecThe Hive
Machine learning is at the core of Pinterest. Pinterest personalizes and ranks 1B+ pins, 700+ million boards for 100M+ users all over the world, using data gathered from collaborative filtering, user curation, web crawling, and more. At Pinterest we model relationships between pins, handle cold-start problems and deal with real-time recommendations.
In this presentation Jure gave an overview of the problems and effective solutions developed at Pinterest. He focused on systems and effective engineering choices made to enable productive machine learning development and enable multiple engineers effectively develop, test, and deploy machine-learned models.
Deezer - Big data as a streaming serviceJulie Knibbe
40 million songs, albums and artists available - how nice? Streaming allows you to get a grasp at the biggest music collections in the world. The only thing is that you would need centuries to listen to all of it.
Getting access doesn’t mean knowing what to do with it. How are we making music discovery more & more efficient at Deezer?
The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using “collective wisdom” distilled from actions of those who worked with this information earlier.
Social information access can be formally defined as a stream of research that explores methods for organizing users' past interaction with an information system (known as explicit and implicit feedback), in order to provide better access to information to the future users of the system. It covers a range of rather different systems and technologies from social navigation to collaborative filtering. An important feature of all social information access systems is self-organization. Social information access systems are able to work with little or no involvement of human indexers, organizers, or other kinds of experts. They are truly powered by a community of users. Due to this feature, social information access technologies are frequently considered as an alternative to the traditional (content-oriented) technologies. The goal of this tutorial is to provide an overview of the emerging social information access research stream and to provide some practical guidelines for building social information access systems.
Congratulations, you have an online community! Odds are, you also have an offline community. Are you using one to strengthen the other?
Most of the organizations I work with in my practice already have all the ingredients in place for a real, vibrant community that lives on and off line. Too often though, on- and offline are treated as separate worlds, with little effort made to bridge the gap. Communities thrive when there is varied and ongoing interaction. Merging physical and non-physical conversations, events, and activities is one of the strongest tactics for building community in the real world.
In this session, we'll talk about how communities form, the ingredients for engagement, the importance of culture, and tactics for bridging the gap.
Takeaways:
- An understanding of the different types and benefits of online and offline communities
- Tactics to kickstart their online and offline communities
- Ways to engage their communities both online and offline
This document provides guidance on conducting online research and evaluating sources. It discusses searching academic databases and filtering search engine results to find reliable information. The document outlines criteria for assessing source credibility, such as considering the author's credentials, publication date, purpose, and references. Students are encouraged to apply the CRAAP test to quickly evaluate whether a source is current, relevant, authoritative, accurate, and objective. The document also contains an in-class activity asking students to find sources on violence in video games, cite them, summarize one, and evaluate its usefulness for an argument on that topic.
Abstract:
Extensive research has been done in prefetching techniques that hide memory latency in microprocessors leading to performance improvements. However, the energy aspect of prefetching is relatively unknown. While aggressive prefetching techniques often help to improve performance, they increase energy consumption by as much as 30% in the memory system. This paper provides a detailed evaluation on the energy impact of hardware data prefetching and then presents a set of new energy-aware techniques to overcome prefetching energy overhead of such schemes. These include compiler-assisted and hardware-based energy-aware techniques and a new power-aware prefetch engine that can reduce hardware prefetching related energy consumption by 7-11 ×. Combined with the effect of leakage energy reduction due to performance improvement, the total energy consumption for the memory system after the application of these techniques can be up to 12% less than the baseline with no prefetching.
operating system calls input and output by (rohit malav)Rohit malav
Introduction of System Call
In computing, a system call is the programmatic way in which a computer program requests a service from the kernel of the operating system it is executed on. A system call is a way for programs to interact with the operating system. A computer program makes a system call when it makes a request to the operating system’s kernel. System call provides the services of the operating system to the user programs via Application Program Interface(API). It provides an interface between a process and operating system to allow user-level processes to request services of the operating system. System calls are the only entry points into the kernel system. All programs needing resources must use system calls.
Services Provided by System Calls :
Process creation and management
Main memory management
File Access, Directory and File system management
Device handling(I/O)
Protection
Networking, etc.
In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.
pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,
built on top of the Python programming language.
Deep learning in python by purshottam vermaRohit malav
In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks and generate predictions with them.
Atm Security System Using Steganography Nss ptt by (rohit malav)Rohit malav
This document describes an ATM security system that uses steganography to securely transmit files. The system encrypts files using a random bit-shift encryption algorithm and embeds them into audio or video files for transmission. At the receiving end, the encrypted file is extracted from the carrier file after entering the correct password. The system aims to provide stronger security than regular encryption by hiding encrypted files within innocent-looking media during transmission.
Samba server Pts report pdf by Rohit malavRohit malav
Samba is a free software re-implementation of the SMB networking protocol, and was originally developed by Andrew Tridgell. Samba provides file and print services for various Microsoft Windows clients and can integrate with a Microsoft Windows Server domain, either as a Domain Controller or as a domain member.
System calls operating system ppt by rohit malavRohit malav
System call
a system call is the programmatic way in which a computer program requests a service from the kernel of the operating system it is executed on. This may include hardware-related services, creation and execution of new processes, and communication with integral kernel services such as process scheduling.
A project on spring framework by rohit malavRohit malav
The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
A key element of Spring is infrastructural support at the application level: Spring focuses on the "plumbing" of enterprise applications so that teams can focus on application-level business logic, without unnecessary ties to specific deployment environments.
android text encryption Network security lab by rohit malavRohit malav
Open the Android Market app on your device and install the Secret Message app. Enter a secret key into the Secret Key box at the top of the app's screen, type the message you want to encrypt into the Message box, tap “Encrypt” and tap “Send via SMS” to send the encrypted message.
samba server setup Pts ppt (rohit malav)Rohit malav
Samba
Samba is a free software re-implementation of the SMB networking protocol, and was originally developed by Andrew Tridgell. Samba provides file and print services for various Microsoft Windows clients and can integrate with a Microsoft Windows Server domain, either as a Domain Controller or as a domain member.
Spring frame work by rohit malav(detailed)Rohit malav
Spring Framework The Spring Framework is an application framework and inversion of control container for the Java platform. The framework's core features can be used by any Java application, but there are extensions for building web applications on top of the Java EE platform detailed in ppt.
The document provides an overview of the Spring framework, including its history, key features, architecture and files used in Spring projects. It discusses how Spring is a lightweight Java application development framework that reduces code and speeds up development. The core features of Spring include inversion of control (IOC) container and aspect-oriented programming (AOP) support. The Spring architecture is made of modular layers including web, data access, ORM and AOP. It also outlines the typical steps for creating a Spring MVC project in Eclipse, including configuring the application context XML, dispatcher servlet XML and web XML files.
Samba server linux (SMB) BY ROHIT MALAVRohit malav
Samba is a free software re-implementation of the SMB networking protocol, and was originally developed by Andrew Tridgell. Samba provides file and print services for various Microsoft Windows clients and can integrate with a Microsoft Windows Server domain, either as a Domain Controller or as a domain member.
The Payroll Management System deals with the financial aspects of employee's salary, allowances, deductions, gross pay, net pay etc. and generation of pay-slips for a specific period. The outstanding benefit of Payroll Management System is its easy implementation.
Payroll system ppt2 (rohit malav) version point 2Rohit malav
The Payroll Management System deals with the financial aspects of employee's salary, allowances, deductions, gross pay, net pay etc. and generation of pay-slips for a specific period. The outstanding benefit of Payroll Management System is its easy implementation.
The document describes an online student management system implemented using C++ that allows storing student data in a text file database. The system provides different views of the data for users like students, faculty, proctors and administrators. It allows adding, editing and viewing student details like registration number, name, marks in subjects and proctor ID. The source code implements the various user interfaces and file handling for performing CRUD operations on the text file database according to the user type.
Digital Unlocked is an initiative by Google in collaboration with the Indian School of Business and Ministry of Electronics and Information Technology to promote digital awareness and to help small scale businesses and startups to go digital in India.[1][2] It was announced and launched by Google's CEO Sundar Pichai during his visit to India in January 2017.[3][4] Digital Unlocked is a training program for small and medium businesses in India. The programme is built across the different formats of online, offline and mobile. The Digital Unlocked's offline training is being conducted in partnership with Federation of Indian Chambers of Commerce & Industry and Indian School of Business.[5][6]
The training program allows the users to set their own goals and then recommends the courses which will help them achieve their own set goals. After completing the goals, or in-between, the users can also choose to complete and learn other courses which are of interest to them. The courses cover a wide range of topics from using the opportunities which the digital media and world have to offer to the advanced tools which can help businesses in many ways. The training program also offer a Certification to those who complete all the courses and qualify in the final assessment.
Rohit android lab projects in suresh gyan viharRohit malav
Overview. 1.1. Labs v Projects. 1.2. Do As Many As You Can. 1.3. Local Lab Setup.
Getting Started.
Android Development.
Android Internals. 4.1. Project: Android OS Monitor. 4.2. Task: IGLearner.
Android Networking.
Build an Android ROM.
KeyLogger.
Malicious Apps.
The document provides code for a Snake game written in Java. It includes requirements for hardware and software, source code for the Snake.java file, and a brief conclusion acknowledging guidance from a professor. The code implements a Snake game with menus for new game, exit, help, and creator details. It uses threads to continuously move the snake forward according to arrow key input and checks for collisions with walls or its body segments.
The document describes an Android app created for Gyan Vihar University. The app aims to provide all important university links and features in one place to save users time. It uses a GridView layout to make the app more visually appealing. The app was created by Sanjeed Alam, a 5th semester student, and brings together key university resources through an interactive mobile interface.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
4. Can Google help?
• Yes, but only when we really know what
we are looking for
• What if I just want some interesting music
tracks?
– Btw, what does it mean by “interesting”?
5. But, what do recommender
systems do, exactly?
1. Predict how much you may like a certain
product/service
2. Compose a list of N best items for you
3. Compose a list of N best users for a certain
product/service
4. Explain to you why these items are recommended to
you
5. Adjust the prediction and recommendation based on
your feedback and other people
20. Want some evidences?
(Celma & Lamere, ISMIR 2007)
• Netflix:
– 2/3 rented movies are from recommendation
• Google News
– 38% more click-through are due to
recommendation
• Amazon
– 35% sales are from recommendation
21. Content-based method
• Web page: words, hyperlinks, images, tags, comments,
titles, URL, topic
• Music: genre, rhythm, melody, harmony, lyrics, meta data,
artists, bands, press releases, expert reviews, loudness,
energy, time, spectrum, duration, frequency, pitch, key,
mode, mood, style, tempo
• User: age, sex, job, location, time, income, education,
language, family status, hobbies, general interests, Web
usage, computer usage, fan club membership, opinion,
comments, tags, mobile usage
• Context: time, location, mobility, activity, socializing,
emotion
22. • Top-N item list:
– Find similar users, collect what they like
– Filter out those the user has rated
– Rank the remaining items by considering
• The number of times each item is liked by those users
• The popularity of the item
• The associated ratings
• The similarity between each item in the list and what the user
has rated
• Switching the role of item to user, we may have
top-N user list
Task 2,3: Top-N recommendation