Product and Quotient Rule Show
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This document discusses rules for finding derivatives of products and quotients using the product rule and quotient rule. It provides examples of applying these rules to functions like f(x)=(x3)(x+x2) and f(x)=(4x2+5)/(x5). It notes that in some cases, it may be easier to first distribute and simplify the expression before finding the derivative.
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The document provides information and examples about solving and graphing inequalities. It defines different types of inequalities using symbols like <, ≤, >, ≥ and explains what they mean. It gives examples of writing inequalities based on number lines and solving simple one-variable inequalities by adding or subtracting values from both sides. The document also discusses absolute value inequalities and how to split them into two cases. Finally, it introduces how to graph linear inequalities in two variables on a coordinate plane.
Power point mulit step
The document outlines the four steps to solve multi-step equations: 1) distribute, 2) combine like terms, 3) get the variable on one side, and 4) isolate the variable. An example problem is worked through to demonstrate these steps. The author reflects that distributing terms was the easiest part of solving multi-step equations, while isolating the variable was the hardest.
Jeopary 3.4-3.6
This document contains a series of math problems involving solving inequalities through addition, subtraction, multiplication, division, and multiple steps. It is broken into sections covering different types of inequalities such as single-variable linear inequalities, inequalities with fractions, inequalities with variables on both sides, and word problems. Each problem provides the inequality, the answer, and sometimes a brief explanation of the steps used to solve it. The document appears to be examples or practice problems for a lesson on solving various types of inequalities.
Core 3 The Product Rule
This document provides examples and instructions for differentiating products using the product rule. It introduces Gottfried Leibniz and his discovery of the product rule in 1675. Examples are given to differentiate products and find the coordinates of a stationary point on a curve using differentiation. The exercises are from a textbook and ask the reader to start on question 3.
Cobb douglas
The document summarizes key aspects of the Cobb-Douglas production function model, including:
1) The functional form of the Cobb-Douglas production function, which exhibits constant returns to scale properties based on labor and capital inputs.
2) How total factor productivity can be measured from the production function as a residual, providing a better measure than partial productivity.
3) A growth accounting formula derived from the logarithmic form of the production function that partitions GDP growth into contributions from labor, capital, and productivity.
4) How the exponent 'a' represents labor's share of output based on marginal productivity theory.
11 x1 t09 03 rules for differentiation (2013)
The document outlines differentiation rules:
1) The derivative of a constant function is 0.
2) The derivative of a function with respect to x multiplied by a constant k is the derivative of the function multiplied by k.
3) The derivative of a polynomial function is found by taking the derivative of each term.
4) The derivative of a function divided by x is the derivative of the function minus the function divided by x squared.
Lesson 10: The Chain Rule (Section 41 slides)
The derivative of a composition of functions is the product of the derivatives of those functions. This rule is important because compositions are so powerful.
CHAIN RULE AND IMPLICIT FUNCTION
This document discusses the chain rule for functions of multiple variables. It begins by reviewing the chain rule for single-variable functions, then extends it to functions of more variables. The chain rule is presented for cases where the dependent variable z is a function of intermediate variables x and y, which are themselves functions of independent variables s and t. General formulas are given using partial derivatives. Examples are worked out, such as finding the derivative of a function defined implicitly by an equation. Diagrams are used to illustrate the relationships between variables.
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The document provides information and examples about solving and graphing inequalities. It defines different types of inequalities using symbols like <, ≤, >, ≥ and explains what they mean. It gives examples of writing inequalities based on number lines and solving simple one-variable inequalities by adding or subtracting values from both sides. The document also discusses absolute value inequalities and how to split them into two cases. Finally, it introduces how to graph linear inequalities in two variables on a coordinate plane.
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The document outlines the four steps to solve multi-step equations: 1) distribute, 2) combine like terms, 3) get the variable on one side, and 4) isolate the variable. An example problem is worked through to demonstrate these steps. The author reflects that distributing terms was the easiest part of solving multi-step equations, while isolating the variable was the hardest.
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This document contains a series of math problems involving solving inequalities through addition, subtraction, multiplication, division, and multiple steps. It is broken into sections covering different types of inequalities such as single-variable linear inequalities, inequalities with fractions, inequalities with variables on both sides, and word problems. Each problem provides the inequality, the answer, and sometimes a brief explanation of the steps used to solve it. The document appears to be examples or practice problems for a lesson on solving various types of inequalities.
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This document provides examples and instructions for differentiating products using the product rule. It introduces Gottfried Leibniz and his discovery of the product rule in 1675. Examples are given to differentiate products and find the coordinates of a stationary point on a curve using differentiation. The exercises are from a textbook and ask the reader to start on question 3.
Cobb douglas
The document summarizes key aspects of the Cobb-Douglas production function model, including:
1) The functional form of the Cobb-Douglas production function, which exhibits constant returns to scale properties based on labor and capital inputs.
2) How total factor productivity can be measured from the production function as a residual, providing a better measure than partial productivity.
3) A growth accounting formula derived from the logarithmic form of the production function that partitions GDP growth into contributions from labor, capital, and productivity.
4) How the exponent 'a' represents labor's share of output based on marginal productivity theory.
11 x1 t09 03 rules for differentiation (2013)
The document outlines differentiation rules:
1) The derivative of a constant function is 0.
2) The derivative of a function with respect to x multiplied by a constant k is the derivative of the function multiplied by k.
3) The derivative of a polynomial function is found by taking the derivative of each term.
4) The derivative of a function divided by x is the derivative of the function minus the function divided by x squared.
Lesson 10: The Chain Rule (Section 41 slides)
The derivative of a composition of functions is the product of the derivatives of those functions. This rule is important because compositions are so powerful.
CHAIN RULE AND IMPLICIT FUNCTION
This document discusses the chain rule for functions of multiple variables. It begins by reviewing the chain rule for single-variable functions, then extends it to functions of more variables. The chain rule is presented for cases where the dependent variable z is a function of intermediate variables x and y, which are themselves functions of independent variables s and t. General formulas are given using partial derivatives. Examples are worked out, such as finding the derivative of a function defined implicitly by an equation. Diagrams are used to illustrate the relationships between variables.
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This document provides an overview of lessons on the chain rule in calculus. It introduces the chain rule for functions of one variable and then extends it to functions of multiple variables. Examples are provided to demonstrate how to use the chain rule to calculate derivatives of composite functions. Formulas for the chain rule are stated for reference. The document also discusses using tree diagrams to visualize applications of the chain rule and introduces matrix expressions of the chain rule.
2.7 chain rule short cuts
The document discusses various chain rules for derivatives, including:
- The power chain rule: [up]' = pup−1(u)'
- Trigonometric chain rules: [sin(u)]' = cos(u)(u)', [cos(u)]' = −sin(u)(u)'
- Examples are provided to demonstrate applying the chain rules to find derivatives of more complex functions like y = sin(x3) and y = sin3(x). Repeated application of the appropriate chain rule at each step is often required.
Core 3 The Quotient Rule
The document discusses finding the equation of the normal line to a curve at a given point using differentiation. It introduces the Quotient Rule for differentiating quotients of functions and notes that the Product Rule is often easier to use. It also attributes the discovery of the Quotient Rule to Gottfried Leibniz in 1677 and provides an example of using it to find stationary points on a curve.
Cobb Douglas production function
The document discusses estimating the parameters of a Cobb-Douglas production function using econometric methods. It provides the equation Q=A*K^α + L^β and estimates the parameters as α = 0.206925 and β = 0.952008 using least squares regression. It finds that changes in K and L explain 95% of the variation in Q. Finally, it determines that the industry exhibits increasing returns to scale since α + β is greater than 1.
Core 3 The Chain Rule
This document discusses using the Chain Rule to differentiate more complicated functions. It introduces the Chain Rule and provides Gottfried Leibniz as the inventor of the notation for derivatives. Two examples are given to demonstrate differentiating functions using the Chain Rule without showing the steps of the work.
Chain Rule
The document discusses the chain rule and how to use it to find derivatives of more complex equations. It provides examples of using the chain rule to take derivatives of functions involving exponents, trigonometric functions, radicals, and combinations of these. Key steps include identifying the inner and outer functions, taking the derivative of the inner function, and plugging into the chain rule formula. The document also contrasts using the chain rule method versus the inside-outside method for some problems.
Lecture 8 derivative rules
This document discusses rules for taking derivatives of various functions including:
1. The derivative of a constant function is 0.
2. The power rule states that the derivative of x^n is nx^{n-1}.
3. Higher derivatives can be found by taking additional derivatives, and the nth derivative is written as f^(n).
It also covers the product rule, quotient rule, and applying rules to polynomials and exponential functions.
The Building Block of Calculus - Chapter 4 The Product Rule and Quotient Rule
The chapter discusses the product rule and quotient rule for derivatives. The product rule states that the derivative of the product of two functions is the derivative of the first function times the second function plus the derivative of the second function times the first function. The quotient rule gives the formula for finding the derivative of the quotient of two functions.
Cobb-douglas production function
The document describes the Cobb-Douglas production function and the results of estimating its parameters. It finds that labor (L) and capital (K) explain 95% of the variation in output (Q) according to the estimated equation Q=-0.31+0.20K+0.95L. Diagnostic tests show the data and estimated model meet the assumptions of regression analysis. Specifically, the variables are stationary and there is no multicollinearity while the model and estimated coefficients are statistically significant with a goodness of fit of 95%. Therefore, the Cobb-Douglas production function appropriately captures the relationship between L, K, and Q in the data.
cobb douglas production function
The Cobb-Douglas production function models the relationship between an output and inputs like labor and capital. It assumes outputs increase with inputs but at a decreasing rate. The formula relates the natural log of output to the natural log of inputs with elasticity coefficients representing the percentage change in output from a 1% change in an input. If the coefficients sum to 1 there are constant returns to scale, less than 1 is decreasing returns, and more than 1 is increasing returns. An example using Taiwan agricultural data from 1958-1972 estimated elasticities of 1.5 for labor and 0.4 for capital, indicating increasing returns to scale.
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This document discusses the Cobb-Douglas production function and its application to estimating production at a lumber company. It begins by defining the Cobb-Douglas production function and its typical form. It then presents the specific production function estimated for Washington-Pacific Lumber, which models lumber output (Q) as a function of labor hours (L), machine hours (K), and energy input (BTUs) (E). It provides the estimated coefficients and standard errors from regressing data. The rest of the document works through examples calculating the effect on output from changes in inputs and determining the returns to scale for this production system based on summing the exponent coefficients.
Cobb douglas production function
The document discusses production functions and their relationships. It shows that a production function relates the maximum quantity of output (Q) that can be produced from given amounts of inputs (capital K and labor L). The production function is represented as Q=f(K,L). It then derives and graphs the equations for total product (Q), average product (APL), and marginal product (MPL) based on the Cobb-Douglas production function of Q=K^0.3 L^0.8. It finds that average product is maximized when average product equals marginal product.
COBB DOUGLAS PRODUCTION FUNCTION THEORY
The Cobb-Douglas production function is widely used to model the relationship between output and two inputs, labor and capital. It takes the form of P(L,K) = B*L^α*K^β, where P is total production, L is labor input, K is capital input, B is total factor productivity, and α and β are output elasticities. The function was formulated by Cobb and Douglas based on statistical evidence showing how U.S. output and the two inputs changed together from 1889-1920. It has since been widely applied despite some criticisms around its lack of microeconomic foundations.
November 14, 2014 compound
This document contains notes from a math class on November 14, 2014 including:
- Topics covered were set-builder notation, compound inequalities, and solving multi-step inequalities.
- Announcements about 1st quarter grades being posted, Khan Academy assignments due, and showing work for class work submissions.
- Examples of solving conjunction and disjunction compound inequalities by graphing the solutions and writing them in set-builder notation.
- Practice problems for students to complete involving compound inequalities.
Powerpoint template 11 15-2010
This document outlines the four steps to solve multi-step equations: 1) distribute, 2) combine like terms, 3) get the variable on one side, and 4) isolate the variable. It provides an example problem working through each step: 7(x+9) - 10 = 2x + 6. Step 1 is to distribute, Step 2 is to combine like terms, Step 3 is to get the variable alone on one side, and Step 4 is to isolate the variable by solving for x.
Powerpoint template 11 15-2010
This document outlines the four steps to solve multi-step equations: 1) distribute, 2) combine like terms, 3) get the variable on one side, and 4) isolate the variable. It provides an example problem working through each step: 7(x+9) - 10 = 2x + 6. Step 1 is to distribute, Step 2 is to combine like terms, Step 3 is to get the variable alone on one side, and Step 4 is to isolate the variable by solving for x.
Simplifying Basic Rational Expressions Part 1
This document provides instructions and examples for simplifying rational expressions by factoring and canceling terms. It begins with definitions of rational expressions and undefined values. Examples shown include finding excluded values that make expressions undefined, as well as simplifying expressions by factoring numerators and denominators and canceling common factors. The goal is to either find excluded values or simplify the expressions into their lowest terms. Ten problems with step-by-step solutions are provided to demonstrate the techniques.
Solving Absolute Value Equations and Inequalities.ppt
This document provides information and examples for solving absolute value equations and inequalities. It begins with definitions of absolute value and discusses how absolute value equations can have two solutions since the expression inside the absolute value can be positive or negative. Examples are provided for solving absolute value equations by setting the expression equal to both its positive and negative values. The document also discusses how to solve absolute value inequalities by splitting them into "and" or "or" statements and provides examples of solving and graphing various absolute value inequalities.
Algebraic expressions lecturenotes_01
This document provides instruction on solving various types of algebraic equations, including:
1) Single-step equations by using the inverse operation (addition/subtraction, multiplication/division)
2) Multi-step equations by isolating the variable term and using inverse operations
3) Equations with variables on both sides by collecting like terms to one side of the equal sign
Worked examples are provided for each type along with checks of the solutions.
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This document provides an overview of lessons on the chain rule in calculus. It introduces the chain rule for functions of one variable and then extends it to functions of multiple variables. Examples are provided to demonstrate how to use the chain rule to calculate derivatives of composite functions. Formulas for the chain rule are stated for reference. The document also discusses using tree diagrams to visualize applications of the chain rule and introduces matrix expressions of the chain rule.
2.7 chain rule short cuts
The document discusses various chain rules for derivatives, including:
- The power chain rule: [up]' = pup−1(u)'
- Trigonometric chain rules: [sin(u)]' = cos(u)(u)', [cos(u)]' = −sin(u)(u)'
- Examples are provided to demonstrate applying the chain rules to find derivatives of more complex functions like y = sin(x3) and y = sin3(x). Repeated application of the appropriate chain rule at each step is often required.
Core 3 The Quotient Rule
The document discusses finding the equation of the normal line to a curve at a given point using differentiation. It introduces the Quotient Rule for differentiating quotients of functions and notes that the Product Rule is often easier to use. It also attributes the discovery of the Quotient Rule to Gottfried Leibniz in 1677 and provides an example of using it to find stationary points on a curve.
Cobb Douglas production function
The document discusses estimating the parameters of a Cobb-Douglas production function using econometric methods. It provides the equation Q=A*K^α + L^β and estimates the parameters as α = 0.206925 and β = 0.952008 using least squares regression. It finds that changes in K and L explain 95% of the variation in Q. Finally, it determines that the industry exhibits increasing returns to scale since α + β is greater than 1.
Core 3 The Chain Rule
This document discusses using the Chain Rule to differentiate more complicated functions. It introduces the Chain Rule and provides Gottfried Leibniz as the inventor of the notation for derivatives. Two examples are given to demonstrate differentiating functions using the Chain Rule without showing the steps of the work.
Chain Rule
The document discusses the chain rule and how to use it to find derivatives of more complex equations. It provides examples of using the chain rule to take derivatives of functions involving exponents, trigonometric functions, radicals, and combinations of these. Key steps include identifying the inner and outer functions, taking the derivative of the inner function, and plugging into the chain rule formula. The document also contrasts using the chain rule method versus the inside-outside method for some problems.
Lecture 8 derivative rules
This document discusses rules for taking derivatives of various functions including:
1. The derivative of a constant function is 0.
2. The power rule states that the derivative of x^n is nx^{n-1}.
3. Higher derivatives can be found by taking additional derivatives, and the nth derivative is written as f^(n).
It also covers the product rule, quotient rule, and applying rules to polynomials and exponential functions.
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The chapter discusses the product rule and quotient rule for derivatives. The product rule states that the derivative of the product of two functions is the derivative of the first function times the second function plus the derivative of the second function times the first function. The quotient rule gives the formula for finding the derivative of the quotient of two functions.
Cobb-douglas production function
The document describes the Cobb-Douglas production function and the results of estimating its parameters. It finds that labor (L) and capital (K) explain 95% of the variation in output (Q) according to the estimated equation Q=-0.31+0.20K+0.95L. Diagnostic tests show the data and estimated model meet the assumptions of regression analysis. Specifically, the variables are stationary and there is no multicollinearity while the model and estimated coefficients are statistically significant with a goodness of fit of 95%. Therefore, the Cobb-Douglas production function appropriately captures the relationship between L, K, and Q in the data.
cobb douglas production function
The Cobb-Douglas production function models the relationship between an output and inputs like labor and capital. It assumes outputs increase with inputs but at a decreasing rate. The formula relates the natural log of output to the natural log of inputs with elasticity coefficients representing the percentage change in output from a 1% change in an input. If the coefficients sum to 1 there are constant returns to scale, less than 1 is decreasing returns, and more than 1 is increasing returns. An example using Taiwan agricultural data from 1958-1972 estimated elasticities of 1.5 for labor and 0.4 for capital, indicating increasing returns to scale.
Presentation on CVP Analysis, Break Even Point & Applications of Marginal Cos...
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Cobb-Douglas Production Function
This document discusses the Cobb-Douglas production function and its application to estimating production at a lumber company. It begins by defining the Cobb-Douglas production function and its typical form. It then presents the specific production function estimated for Washington-Pacific Lumber, which models lumber output (Q) as a function of labor hours (L), machine hours (K), and energy input (BTUs) (E). It provides the estimated coefficients and standard errors from regressing data. The rest of the document works through examples calculating the effect on output from changes in inputs and determining the returns to scale for this production system based on summing the exponent coefficients.
Cobb douglas production function
The document discusses production functions and their relationships. It shows that a production function relates the maximum quantity of output (Q) that can be produced from given amounts of inputs (capital K and labor L). The production function is represented as Q=f(K,L). It then derives and graphs the equations for total product (Q), average product (APL), and marginal product (MPL) based on the Cobb-Douglas production function of Q=K^0.3 L^0.8. It finds that average product is maximized when average product equals marginal product.
COBB DOUGLAS PRODUCTION FUNCTION THEORY
The Cobb-Douglas production function is widely used to model the relationship between output and two inputs, labor and capital. It takes the form of P(L,K) = B*L^α*K^β, where P is total production, L is labor input, K is capital input, B is total factor productivity, and α and β are output elasticities. The function was formulated by Cobb and Douglas based on statistical evidence showing how U.S. output and the two inputs changed together from 1889-1920. It has since been widely applied despite some criticisms around its lack of microeconomic foundations.
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- Examples of solving conjunction and disjunction compound inequalities by graphing the solutions and writing them in set-builder notation.
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This document outlines the four steps to solve multi-step equations: 1) distribute, 2) combine like terms, 3) get the variable on one side, and 4) isolate the variable. It provides an example problem working through each step: 7(x+9) - 10 = 2x + 6. Step 1 is to distribute, Step 2 is to combine like terms, Step 3 is to get the variable alone on one side, and Step 4 is to isolate the variable by solving for x.
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Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
20 Comprehensive Checklist of Designing and Developing a Website
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsArtificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
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Product and Quotient Rule Show
- 1. Sam Walsh
- 3. Product Rule Use to find derivative of a product
- 4. Product Rule continued… • Take this equation: Set it up like:
- 5. Product Rule continued… •Plug things in to get a new equation: ANSWER:
- 6. Quotient Rule Use to find the derivative of a quotient
- 7. Quotient Rule continued… take this equation:
- 8. Quotient Rule Continued… Plug it in:
- 9. Quotient Rule Continued… Simplify!
- 10. Practice •f(x)= (x3)(x+x2) • f(x)= (4x2 +5)/(x5) •f(x)= (2x9 + 3x2)(4x3) Challenge: • f(x)= (7x2)(x4)/(3x5) * Click on each problem to get the answer * Also please note there are easier ways to do these! ( look at last slide)
- 11. The Easier Way… Problem 1: (x3)(x + x2) Distribute x4 + x5 Then find the derivative directly 4x3 + 5x4 Problem 2: (4x2 + 5)/x5 Distribute 4x-3 + 5x-5 Find the derivative -12x-4 – 25x-6 And so on… Lesson: You might be able to simplify!
- 12. Answers f’(x)= 4x3 + 5x4
- 13. Answers f’(x)= -12x-4- 25x-6
- 14. Answers f’(x)= 96x11 + 60x4
- 15. Answers f’(x)= (21/9)