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Applications of Derivatives

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The derivative defines the rate at which one variable changes with respect to another. It is an important concept that comes in extremely useful in many applications: in everyday life, the derivative can tell you at which speed you are driving, or help you predict fluctuations on the stock market; in machine learning, derivatives are important for function optimization. This tutorial will explore different applications of derivatives, starting with the more familiar ones before moving to machine learning. We will be taking a closer look at what the derivatives tell us about the different functions we are studying. In this tutorial, you will discover different applications of derivatives.


Optimization Algorithm Using Matlab

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I'm very glad to have opportunity to teach you one of the most popular and powerful optimization algorithms in this course. If you search FireFly optimization algorithm in google scholar, it could be seen that there are many vast range of papers has been published by implementing this optimization algorithm in different fields of science. In this course, after presenting the mathematical concept of each part of the considered optimization algorithm, I write its code immediately in matlab. All of the written codes are available, however, I strongly suggest to write the codes with me. Notice that, if you don't have matlab or you know another programming language, don't worry at all.


Taplytics Introduces Taplytics AI, a New AI-Optimization Engine for Personalized Digital Experiences

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The Taplytics AI family's first launch, Genius AI, enables advanced product teams to create personalized copy for any intended persona quickly. Marketing and product teams can use Genius AI to pick any text-based aspect on a webpage and obtain AI-powered copy recommendations to improve site conversion. Complete webpages can be personalized to communicate directly to any preferred persona or primary customers without jotting a single line of code or needing any resources to develop. Businesses can use Taplytics' Genius AI to write landing page copy for a wide range of personas, including retail customers, financial firms, food delivery customers, and much more. Businesses can use Genius AI to develop tailored experiences for any particular audience by using distinctive customer characteristics as inputs to the model.


Why Data Scientists Should Learn Dynamic Programming

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DP is a type of algorithm that breaks down problems into sub-problems and stores and reuses the results from the previous calculations. We shall introduce what a recursion is before DP. A recursive function is a function defined in relation to itself, which means the function will continue to call itself until some condition is met. A recursion contains two parts: a base case and a recursive case. The function will keep iterating the recursive case and calculate the results to sub-problems until the base case is met.


Quantum computing: This new 100-qubit processor is built with atoms cooled down near to absolute zero

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The company's 100-qubit gate-based quantum computer, code-named Hilbert, is launching later this year after final tuning and optimization work. By cooling atoms down to near absolute zero and then controlling them with lasers, a company has successfully created a 100-qubit quantum processor that compares to the systems developed by leading quantum players to date. ColdQuanta, a US-based company that specializes in the manipulation of cold atoms, unveiled the new quantum processor unit, which will form the basis of the company's 100-qubit gate-based quantum computer, code-named Hilbert, launching later this year after final tuning and optimization work. There are various different approaches to quantum computing, and among those that have risen to prominence in the last few years feature superconducting systems, trapped ions, photonic quantum computers and even silicon spin qubits. Cold atoms, on the other hand, haven't made waves in the quantum ecosystem so far.


US Air Force pilots get an artificial intelligence assist with scheduling aircrews

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Take it from U.S. Air Force Captain Kyle McAlpin when he says that scheduling C-17 aircraft crews is a headache. An artificial intelligence research flight commander for the Department of Air Force–MIT AI Accelerator Program, McAlpin is also an experienced C-17 pilot. "You could have a mission change and spend the next 12 hours of your life rebuilding a schedule that works," he says. It's a pain point for crew of 52 squadrons who operate C-17s, the military cargo aircraft that transport troops and supplies globally. This year, the Air Force marked 4 million flight hours for its C-17 fleet, which comprises 275 U.S. and allied aircraft.


Gradient Descent With AdaGrad From Scratch

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Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that it uses the same step size (learning rate) for each input variable. This can be a problem on objective functions that have different amounts of curvature in different dimensions, and in turn, may require a different sized step to a new point. Adaptive Gradients, or AdaGrad for short, is an extension of the gradient descent optimization algorithm that allows the step size in each dimension used by the optimization algorithm to be automatically adapted based on the gradients seen for the variable (partial derivatives) seen over the course of the search. In this tutorial, you will discover how to develop the gradient descent with adaptive gradients optimization algorithm from scratch.


Council Post: Four Key Differences Between Mathematical Optimization And Machine Learning

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Edward Rothberg is CEO and Co-Founder of Gurobi Optimization, which produces the world's fastest mathematical optimization solver. This is a question that -- as the CEO of a mathematical optimization software company -- I get asked all the time. Although it seems like a simple question, it's actually quite difficult to come up with a concise, coherent answer. Indeed, mathematical optimization and machine learning are two tools that at first glance -- like scissors and pliers -- may seem to have a lot in common. When you look closely at their fundamental features and actual applications, however, you'll see some important differences.


Gradient Descent Optimization With AdaMax From Scratch

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Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that a single step size (learning rate) is used for all input variables. Extensions to gradient descent, like the Adaptive Movement Estimation (Adam) algorithm, use a separate step size for each input variable but may result in a step size that rapidly decreases to very small values. AdaMax is an extension to the Adam version of gradient descent that generalizes the approach to the infinite norm (max) and may result in a more effective optimization on some problems. In this tutorial, you will discover how to develop gradient descent optimization with AdaMax from scratch.


A Gentle Introduction to Premature Convergence

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Population-based optimization algorithms, like evolutionary algorithms and swarm intelligence, often describe their dynamics in terms of the interplay between selective pressures and convergence. For example, strong selective pressures result in faster convergence and likely premature convergence. Weaker selective pressures may result in a slower convergence (greater computational cost) although perhaps locate a better or even global optima. An operator with a high selective pressure decreases diversity in the population more rapidly than operators with a low selective pressure, which may lead to premature convergence to suboptimal solutions. A high selective pressure limits the exploration abilities of the population.