IB Maths. Graphs of a funtion and its derivativeestelav
This document discusses how to draw sketches of the graphs of the first and second derivatives of a given function. It includes graphs of a quadratic function and its derivatives, asking the reader to identify each function. Additional resources like worksheets are provided to help readers practice drawing derivative graphs and solving related puzzles.
The document discusses an MBA design project focused on developing an affordable design school. It notes the project had a $1500 budget and 24 students. Key steps included brainstorming extreme affordability solutions, identifying an initial concept of a design school with only two teachers, and refining the concept based on feedback. The document explores challenges around costs, teacher salaries, and ensuring quality with few resources before concluding the proposed affordable design school concept was feasible.
This document provides steps for factoring a quadratic equation of the form ax^2 + bx + c. It uses the example equation 2x^2 + 15x + 18. The steps are: 1) Identify the coefficients a, b, c. 2) Multiply a and c and add to b to find the first two terms' common factor. 3) Group the terms and factorize the equation into the form (ax + b)(cx + d). The example is factorized as (1x+6)(2x+3).
Machine learning is a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The document discusses machine learning and artificial intelligence, providing an overview of the topics as well as contact information for Alexandre Uehara, an expert in the field.
Trasparencias de la charla Machine Learning for Dummies del grupo Meetup de Azuges @ 22 de Noviembre de 2016
Ponentes: Rodrigo Cabello y Carlos Landeras
This document provides an incomplete history of machine learning from 1946 to 2016. It describes some of the major developments in the field including the first general purpose computer (ENIAC), Arthur Samuel creating the first machine learning program to play checkers in 1955, the development of the perceptron in 1958, Marvin Minsky's influential work establishing limits of perceptrons, the AI winter from 1970-1980, the rediscovery of backpropagation in the 1980s reigniting neural networks research, support vector machines gaining popularity in the 1990s, IBM's Deep Blue beating Garry Kasparov at chess in 1997, advances in image recognition with challenges like ImageNet, AlphaGo defeating top Go players in 2016, and Geoffrey Hinton's vision
This document provides an introduction to machine learning, including:
- It discusses how the human brain learns to classify images and how machine learning systems are programmed to perform similar tasks.
- It provides an example of image classification using machine learning and discusses how machines are trained on sample data and then used to classify new queries.
- It outlines some common applications of machine learning in areas like banking, biomedicine, and computer/internet applications. It also discusses popular machine learning algorithms like Bayes networks, artificial neural networks, PCA, SVM classification, and K-means clustering.
IB Maths. Graphs of a funtion and its derivativeestelav
This document discusses how to draw sketches of the graphs of the first and second derivatives of a given function. It includes graphs of a quadratic function and its derivatives, asking the reader to identify each function. Additional resources like worksheets are provided to help readers practice drawing derivative graphs and solving related puzzles.
The document discusses an MBA design project focused on developing an affordable design school. It notes the project had a $1500 budget and 24 students. Key steps included brainstorming extreme affordability solutions, identifying an initial concept of a design school with only two teachers, and refining the concept based on feedback. The document explores challenges around costs, teacher salaries, and ensuring quality with few resources before concluding the proposed affordable design school concept was feasible.
This document provides steps for factoring a quadratic equation of the form ax^2 + bx + c. It uses the example equation 2x^2 + 15x + 18. The steps are: 1) Identify the coefficients a, b, c. 2) Multiply a and c and add to b to find the first two terms' common factor. 3) Group the terms and factorize the equation into the form (ax + b)(cx + d). The example is factorized as (1x+6)(2x+3).
Machine learning is a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The document discusses machine learning and artificial intelligence, providing an overview of the topics as well as contact information for Alexandre Uehara, an expert in the field.
Trasparencias de la charla Machine Learning for Dummies del grupo Meetup de Azuges @ 22 de Noviembre de 2016
Ponentes: Rodrigo Cabello y Carlos Landeras
This document provides an incomplete history of machine learning from 1946 to 2016. It describes some of the major developments in the field including the first general purpose computer (ENIAC), Arthur Samuel creating the first machine learning program to play checkers in 1955, the development of the perceptron in 1958, Marvin Minsky's influential work establishing limits of perceptrons, the AI winter from 1970-1980, the rediscovery of backpropagation in the 1980s reigniting neural networks research, support vector machines gaining popularity in the 1990s, IBM's Deep Blue beating Garry Kasparov at chess in 1997, advances in image recognition with challenges like ImageNet, AlphaGo defeating top Go players in 2016, and Geoffrey Hinton's vision
This document provides an introduction to machine learning, including:
- It discusses how the human brain learns to classify images and how machine learning systems are programmed to perform similar tasks.
- It provides an example of image classification using machine learning and discusses how machines are trained on sample data and then used to classify new queries.
- It outlines some common applications of machine learning in areas like banking, biomedicine, and computer/internet applications. It also discusses popular machine learning algorithms like Bayes networks, artificial neural networks, PCA, SVM classification, and K-means clustering.
The document summarizes key concepts in machine learning, including defining learning, types of learning (induction vs discovery, guided learning vs learning from raw data, etc.), generalisation and specialisation, and some simple learning algorithms like Find-S and the candidate elimination algorithm. It discusses how learning can be viewed as searching a generalisation hierarchy to find a hypothesis that covers the examples. The candidate elimination algorithm maintains the version space - the set of hypotheses consistent with the training examples - by updating the general and specific boundaries as new examples are processed.
Cost savings from auto-scaling of network resources using machine learningSabidur Rahman
1. The document discusses using machine learning techniques to automatically scale network resources based on traffic load in order to reduce costs. It provides background on auto-scaling in cloud computing and motivations for applying it to network functions.
2. A literature review covers prior work on using auto-scaling for content distribution networks and data center networks to improve energy efficiency.
3. The problem is defined as minimizing network operation costs through auto-scaling virtual network resources based on predicted traffic loads while meeting service level agreements and policies. A high-level design is proposed using a traffic prediction module, auto-scale controller, and actuators to adjust resources.
Lecture 9 - Machine Learning and Support Vector Machines (SVM)Sean Golliher
This document discusses machine learning and support vector machines. It provides examples of using probabilities to determine the likelihood of a document being relevant given certain terms. It also discusses language models and smoothing techniques used in document ranking. Finally, it briefly outlines different types of machine learning problems and algorithms like supervised learning, classification, and reinforcement learning.
The document discusses the history and development of Monte Carlo simulation methods in financial engineering. Some key points:
1) Monte Carlo simulation techniques originated from games of chance and probabilistic concepts in the 17th century. They were later applied to calculating integrals and solving differential equations.
2) In the 1940s/50s, the techniques were developed and applied at Los Alamos National Laboratory, coining the term "Monte Carlo."
3) In the 1970s, Monte Carlo methods became widely used in finance, with Black-Scholes options pricing and models simulating random asset price movements. They allow calculating expected option payoffs and fair values.
Applications of Machine Learning to Location-based Social NetworksJoan Capdevila Pujol
This document summarizes an application of machine learning techniques to location-based social networks. It discusses two applications:
1) GeoSRS, a hybrid social recommender system that provides personalized venue recommendations to users. It extracts data from Foursquare using an API, performs text modeling on tip content, and generates recommendations using both collaborative and content-based approaches.
2) Tweet-SCAN, an event discovery technique that identifies dense groups of geolocated tweets close in space, time, and topic to discover real-world events. It extends the DBSCAN clustering algorithm and represents tweet topics using probabilistic models. The technique is evaluated on tweets from Barcelona events.
This document provides a brief history of Markov chain Monte Carlo (MCMC) methods. It describes how MCMC originated from early Monte Carlo methods developed during World War II to simulate nuclear weapons. The first true MCMC algorithm, known as the Metropolis algorithm, was published in 1953 and aimed to sample from complicated probability distributions by constructing a Markov chain with a desired stationary distribution. However, MCMC methods did not gain widespread use in statistics until the late 1980s and early 1990s, partly due to lack of computing power and understanding of Markov chains.
Este documento introduce los métodos de Monte Carlo y Monte Carlo por cadenas de Markov para la integración numérica. Estos métodos generan puntos aleatorios en lugar de puntos equiespaciados para aproximar integrales. El método de Monte Carlo directo tiene un error que depende de la raíz cuadrada del número de puntos, mientras que el error de otros métodos depende del número de dimensiones. Monte Carlo por cadenas de Markov genera una cadena de configuraciones cuya distribución corresponde a la distribución deseada mediante reglas de transición.
The Internet of Things (IoT) comes with great possibilities as well as major security and privacy issues. Although digital forensics has long been studied in both academia and industry, mobility forensics is relatively new and unexplored. Mobility forensics deals with tools and techniques that work towards forensically sound recovery of data and evidence from mobile devices [1]. In this paper, we explore mobility forensics in the context of IoT. This paper discusses the data collection and classification process from IoT smart home devices in details. It also contains attack scenario based analysis of collected data and a proposed mobility forensics model that fits into such scenarios.
Cite: K. M. S. Rahman, M. Bishop, and A. Holt, “Internet of Things Mobility Forensics,” INSuRE Conference, 2016.
This is an intro talk about data visualization, focused on showing few basic concepts on data visualization.
Presented during 1st Machine Learning Meetup - Porto Alegre - 1st June 2016
Presenter - Roberto Silveira
Airline passenger profiling based on fuzzy deep machine learningAyman Qaddumi
Passenger profiling plays a vital part of commercial aviation security. Classical passenger profiling methods are inefficient in handling the rapidly increasing amounts of electronic records. Emerging deep learning models combined with highly parallel computing have exhibited promising performance for feature exaction and abstraction, but their applications in aviation security management have rarely been reported.
This document is a technical report submitted as part of a Master's degree in Information Security. It examines applying machine learning algorithms to the task of intrusion detection in computer security. Specifically, it analyzes the NBTree and VFI machine learning algorithms on a dataset of network connections and compares their performance at detecting intrusions. The NBTree algorithm achieved high accuracy and recall, indicating it is well-suited for intrusion detection using machine learning. The report also discusses future work and the usefulness of machine learning for computer security problems.
Online Machine Learning: introduction and examplesFelipe
In this talk I introduce the topic of Online Machine Learning, which deals with techniques for doing machine learning in an online setting, i.e. where you train your model a few examples at a time, rather than using the full dataset (off-line learning).
This document discusses error analysis for quasi-Monte Carlo methods. It introduces the trio error identity that decomposes the error into three terms: the variation of the integrand, the discrepancy of the sampling measure from the probability measure, and the alignment between the integrand and the difference between the measures. Several examples are provided to illustrate the identity, including integration over a reproducing kernel Hilbert space. The discrepancy term can be evaluated in O(n^2) operations and converges at different rates depending on the sampling method and properties of the integrand.
BSidesLV 2013 - Using Machine Learning to Support Information SecurityAlex Pinto
Big Data, Data Science, Machine Learning and Analytics are a few of the new buzzwords that have invaded out industry of late. Again we are being sold a unicorn-laden, silver-bullet panacea by heavy handed marketing folks, evoking an expected pushback from the most enlightened members of our community. However, as was the case before, there might just be enough technical meat in there to help out with our security challenges and the overwhelming odds we face everyday. And if so, what do we as a community have to know about these technologies in order to be better professionals? Can we really use the data we have been collecting to help automate our security decision making? Is a robot going to steal my job?
If you are interested in what is behind this marketing buzz and are not scared of a little math, this talk would like to address some insights into applying Machine Learning techniques to data any of us have easy access to, and try to bring home the point that if all of this technology can be used to show us “better” ads in social media and track our behavior online (and a bit more than that) it can also be used to defend our networks as well.
Machine Learning in 5 Minutes— ClassificationBrian Lange
Slides from a lightning talk on classification methods, originally given at Open Source Open Mic Chicago 01/2016. Yes, I know I left things you. You try covering this in 5 minutes.
Support Vector Machines are a type of machine learning algorithm that can automatically create a model to classify data based on training examples. The model represents data as points in space and finds the optimal hyperplane that separates categories of data by the widest possible margin. SVMs use kernels to project data into a higher dimensional space to allow for the discovery of more complex patterns that would not be possible in the original input space.
Machine Learning Introduction for Digital Business LeadersSudha Jamthe
This is Sudha Jamthe's lecture to the Masters program students of Barcelona Technology School.
Covers Machine Learning introduction of technology foundation, use cases across multiple industries, jobs and varioys business roles to create Machine Intelligence Products and Services.
The document discusses email retargeting for an online fashion brand in India. The brand targeted app users with personalized emails recommending relevant products and offers based on what users viewed and purchased previously. This approach led to a 33% higher click rate and 40% better open rate for retargeted emails compared to generic emails. The document also provides an overview of how email retargeting works, including collecting user data from the website and app, sending targeted emails with product reminders, and examples of retargeting emails on mobile and desktop.
The document summarizes key concepts in machine learning, including defining learning, types of learning (induction vs discovery, guided learning vs learning from raw data, etc.), generalisation and specialisation, and some simple learning algorithms like Find-S and the candidate elimination algorithm. It discusses how learning can be viewed as searching a generalisation hierarchy to find a hypothesis that covers the examples. The candidate elimination algorithm maintains the version space - the set of hypotheses consistent with the training examples - by updating the general and specific boundaries as new examples are processed.
Cost savings from auto-scaling of network resources using machine learningSabidur Rahman
1. The document discusses using machine learning techniques to automatically scale network resources based on traffic load in order to reduce costs. It provides background on auto-scaling in cloud computing and motivations for applying it to network functions.
2. A literature review covers prior work on using auto-scaling for content distribution networks and data center networks to improve energy efficiency.
3. The problem is defined as minimizing network operation costs through auto-scaling virtual network resources based on predicted traffic loads while meeting service level agreements and policies. A high-level design is proposed using a traffic prediction module, auto-scale controller, and actuators to adjust resources.
Lecture 9 - Machine Learning and Support Vector Machines (SVM)Sean Golliher
This document discusses machine learning and support vector machines. It provides examples of using probabilities to determine the likelihood of a document being relevant given certain terms. It also discusses language models and smoothing techniques used in document ranking. Finally, it briefly outlines different types of machine learning problems and algorithms like supervised learning, classification, and reinforcement learning.
The document discusses the history and development of Monte Carlo simulation methods in financial engineering. Some key points:
1) Monte Carlo simulation techniques originated from games of chance and probabilistic concepts in the 17th century. They were later applied to calculating integrals and solving differential equations.
2) In the 1940s/50s, the techniques were developed and applied at Los Alamos National Laboratory, coining the term "Monte Carlo."
3) In the 1970s, Monte Carlo methods became widely used in finance, with Black-Scholes options pricing and models simulating random asset price movements. They allow calculating expected option payoffs and fair values.
Applications of Machine Learning to Location-based Social NetworksJoan Capdevila Pujol
This document summarizes an application of machine learning techniques to location-based social networks. It discusses two applications:
1) GeoSRS, a hybrid social recommender system that provides personalized venue recommendations to users. It extracts data from Foursquare using an API, performs text modeling on tip content, and generates recommendations using both collaborative and content-based approaches.
2) Tweet-SCAN, an event discovery technique that identifies dense groups of geolocated tweets close in space, time, and topic to discover real-world events. It extends the DBSCAN clustering algorithm and represents tweet topics using probabilistic models. The technique is evaluated on tweets from Barcelona events.
This document provides a brief history of Markov chain Monte Carlo (MCMC) methods. It describes how MCMC originated from early Monte Carlo methods developed during World War II to simulate nuclear weapons. The first true MCMC algorithm, known as the Metropolis algorithm, was published in 1953 and aimed to sample from complicated probability distributions by constructing a Markov chain with a desired stationary distribution. However, MCMC methods did not gain widespread use in statistics until the late 1980s and early 1990s, partly due to lack of computing power and understanding of Markov chains.
Este documento introduce los métodos de Monte Carlo y Monte Carlo por cadenas de Markov para la integración numérica. Estos métodos generan puntos aleatorios en lugar de puntos equiespaciados para aproximar integrales. El método de Monte Carlo directo tiene un error que depende de la raíz cuadrada del número de puntos, mientras que el error de otros métodos depende del número de dimensiones. Monte Carlo por cadenas de Markov genera una cadena de configuraciones cuya distribución corresponde a la distribución deseada mediante reglas de transición.
The Internet of Things (IoT) comes with great possibilities as well as major security and privacy issues. Although digital forensics has long been studied in both academia and industry, mobility forensics is relatively new and unexplored. Mobility forensics deals with tools and techniques that work towards forensically sound recovery of data and evidence from mobile devices [1]. In this paper, we explore mobility forensics in the context of IoT. This paper discusses the data collection and classification process from IoT smart home devices in details. It also contains attack scenario based analysis of collected data and a proposed mobility forensics model that fits into such scenarios.
Cite: K. M. S. Rahman, M. Bishop, and A. Holt, “Internet of Things Mobility Forensics,” INSuRE Conference, 2016.
This is an intro talk about data visualization, focused on showing few basic concepts on data visualization.
Presented during 1st Machine Learning Meetup - Porto Alegre - 1st June 2016
Presenter - Roberto Silveira
Airline passenger profiling based on fuzzy deep machine learningAyman Qaddumi
Passenger profiling plays a vital part of commercial aviation security. Classical passenger profiling methods are inefficient in handling the rapidly increasing amounts of electronic records. Emerging deep learning models combined with highly parallel computing have exhibited promising performance for feature exaction and abstraction, but their applications in aviation security management have rarely been reported.
This document is a technical report submitted as part of a Master's degree in Information Security. It examines applying machine learning algorithms to the task of intrusion detection in computer security. Specifically, it analyzes the NBTree and VFI machine learning algorithms on a dataset of network connections and compares their performance at detecting intrusions. The NBTree algorithm achieved high accuracy and recall, indicating it is well-suited for intrusion detection using machine learning. The report also discusses future work and the usefulness of machine learning for computer security problems.
Online Machine Learning: introduction and examplesFelipe
In this talk I introduce the topic of Online Machine Learning, which deals with techniques for doing machine learning in an online setting, i.e. where you train your model a few examples at a time, rather than using the full dataset (off-line learning).
This document discusses error analysis for quasi-Monte Carlo methods. It introduces the trio error identity that decomposes the error into three terms: the variation of the integrand, the discrepancy of the sampling measure from the probability measure, and the alignment between the integrand and the difference between the measures. Several examples are provided to illustrate the identity, including integration over a reproducing kernel Hilbert space. The discrepancy term can be evaluated in O(n^2) operations and converges at different rates depending on the sampling method and properties of the integrand.
BSidesLV 2013 - Using Machine Learning to Support Information SecurityAlex Pinto
Big Data, Data Science, Machine Learning and Analytics are a few of the new buzzwords that have invaded out industry of late. Again we are being sold a unicorn-laden, silver-bullet panacea by heavy handed marketing folks, evoking an expected pushback from the most enlightened members of our community. However, as was the case before, there might just be enough technical meat in there to help out with our security challenges and the overwhelming odds we face everyday. And if so, what do we as a community have to know about these technologies in order to be better professionals? Can we really use the data we have been collecting to help automate our security decision making? Is a robot going to steal my job?
If you are interested in what is behind this marketing buzz and are not scared of a little math, this talk would like to address some insights into applying Machine Learning techniques to data any of us have easy access to, and try to bring home the point that if all of this technology can be used to show us “better” ads in social media and track our behavior online (and a bit more than that) it can also be used to defend our networks as well.
Machine Learning in 5 Minutes— ClassificationBrian Lange
Slides from a lightning talk on classification methods, originally given at Open Source Open Mic Chicago 01/2016. Yes, I know I left things you. You try covering this in 5 minutes.
Support Vector Machines are a type of machine learning algorithm that can automatically create a model to classify data based on training examples. The model represents data as points in space and finds the optimal hyperplane that separates categories of data by the widest possible margin. SVMs use kernels to project data into a higher dimensional space to allow for the discovery of more complex patterns that would not be possible in the original input space.
Machine Learning Introduction for Digital Business LeadersSudha Jamthe
This is Sudha Jamthe's lecture to the Masters program students of Barcelona Technology School.
Covers Machine Learning introduction of technology foundation, use cases across multiple industries, jobs and varioys business roles to create Machine Intelligence Products and Services.
The document discusses email retargeting for an online fashion brand in India. The brand targeted app users with personalized emails recommending relevant products and offers based on what users viewed and purchased previously. This approach led to a 33% higher click rate and 40% better open rate for retargeted emails compared to generic emails. The document also provides an overview of how email retargeting works, including collecting user data from the website and app, sending targeted emails with product reminders, and examples of retargeting emails on mobile and desktop.
What do personalized Push Notifications look like? Which elements in a Push can be customized? What kind of Mobile Marketing goals can you achieve with such personalized Push messages? Here's the answer to all of these questions.
The document discusses 4 ways to segment users for marketing purposes:
1. Use pre-defined segments that are standard for a particular industry or target common use cases.
2. Create rule-based segments based on specific marketing goals and analytics to target niche audiences.
3. Use machine learning to identify segments that are most likely to engage and convert based on customer data to predict potential buyers.
4. Evaluate marketing hypotheses by running campaigns on specific user segments through discovery to test any hypothesis and use historical data to create new segments.
App user engagement, display retargeting, push notifications, fight off uninstalls, social, email - Do you use different tools to action multiple App marketing strategies? This makes App marketing complicated, it impacts your ROI as well.Try Engage App – the only Mobile Marketing Platform that allows you to personalize cross-channel marketing for your App and grow App conversion rate upto 2X.
Here's a cool infographic that shows how all of this works.
Your customers interact with your business in many ways. It may be your offline channels or Online. They leave behind transactional data that is stored in your marketing compartments. Here is how you make sure you don't bombard them with different messages in different channels
The document summarizes success stories from clients that used Vizury's mobile app retargeting and re-engagement solutions. It describes three clients: 1) A large online fashion retailer in India that saw a 5% contribution to in-app listings and 3x higher in-app conversion rates. 2) Another leading online fashion retailer in Asia that saw 18% contribution to in-app transactions and 2x higher conversion rates. 3) The largest music streaming app in India that saw 70% lower user reactivation costs and 2x more user reactivation volume compared to Facebook.
The document describes a case study of a large online fashion retailer in India that wanted to increase in-app purchases on their mobile app. Vizury provided a solution to retarget app users who dropped off by using SDK-less integration to collect data and deep linking banners to product pages. Their solution was compatible with both Android and iOS and integrated with major mobile app publishers. The results were a 5% contribution to in-app listings from Vizury and a 3x higher in-app conversion rate during the Vizury campaign.
Here are 5 things that every mobile marketer should know. We have compiled a list from the awesome sessions at an event- Beyond Installs. Great app marketers have shared their tips. Check it out!
How do you keep your customers engaged at so many touch points and ensure that they come back? How do you justify the money you are spending?
A cross-channel marketing strategy with data at the crux is the answer. This presentation explains the 5 things Airline Marketers should do today.
“If your plans don’t include mobile, your plans aren’t complete”, says Wendy Clarke, VP Marketing, Coca-Cola. As much as it adds to your marketing plans, mobile complicates things with its web and app interfaces, iOS and Android platforms. But your customers are fragmented across these platforms.
Here’s our Shiju Mathew, Head Products- Mobile at Vizury talking about the fragmented mobile landscape and the need for effective user-mapping.
Have you wished that your social media efforts could get you more than just “Likes”? Can your Facebook campaign spike up your website sales? With more than 75% of the world on Facebook, its power to help businesses grow is unimaginable. Here's Facebook Retargeting broken down for you.
In the year 2013, nearly 20–50 per cent of all online purchases were done via mobile devices. Advertisers have been quick to notice this shifting trend and are evolving their marketing plan with mobile as an integral part. However, one of the biggest challenges is choosing the right mobile retargeting partner or evaluating how well the current partner is equipped to handle the intricacies of mobile retargeting.
While the decision is not an easy one, here are 10 tips that could be considered while choosing the apt mobile retargeting partner…
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.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
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.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
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.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
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27. Query Time Estimator
● Willcover ONLY mathematics. (not Hive)
● Estimating query time of hive
● On abstract Level . . .
● It is again similarto learning y = ax + b as described earlier.But
slightlydifferently.
● y= (d.j).w1 + (t/vc)w2 + t.w3 + d.w4+j.w5+ w0
● d=depth of sd tree , j= pending jobs , vc=vcores/running jobs,
● t=total cpu-time, y= estimated execution time
● w=[w0 w1 w2 w3 w4 w5] and var(w)=[0.05 0.08 0.1 0.1 0.03
28. Challenges
● Lot of Noise and ambiguity
● Instantaneous Nature of solution.
● Two experiments conducted
● 1. Random Forest Based
● 2. Monte Carlo based Particle Filter