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Optimization


Evolutionary Algorithms

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With this approach, candidate solutions to an optimization problem are randomly generated and act as individuals interacting with a larger population. A fitness function determines the quality of the solutions the candidates find as they move about in each iteration. The "best fit" individuals are then chosen for reproduction in the next iteration. This generational process is repeated until the algorithm has evolved to find the optimal solution to the problem.


Introduction to Reinforcement Learning (RL) -- Part 4 -- "Dynamic Programming"

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Starting in this chapter, the assumption is that the environment is a finite Markov Decision Process (finite MDP). In this chapter we'll see how we can use DP algorithms to compute the value functions in a slightly different, less intractable way. The general idea is to take these 2 equations, and turn them into update rules for for improving the approximations of our value functions. It will make more sense later on. Policy Evaluation Policy evaluation means computing the state-value function Vπ for an arbitrary policy π.


Why Learned Optimizers Outperform "hand-designed" Optimizers like Adam

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Optimizers, such as momentum (Polyak, 1964), AdaGrad (Duchi et al., 2011), RMSProp (Tieleman & Hinton, 2012), or Adam (Kingma & Ba, 2014), are algorithms underlying in nearly all machine learning. Combined with the loss function, they are the key pieces that enable machine learning to work. These algorithms use simple update rules derived from intuitive mechanisms and theoretical principles, a mathematical way of measuring how wrong your predictions are, and tune it to become better. Recent research thread has focused on learning-based optimization algorithms; they called it learned optimizers. It has been shown that learned optimizers outperform "hand-designed" optimizers, like Adam, by directly parameterizing and training an optimizer on the distribution of tasks (Andrychowicz et al., 2016; Wichrowska et al., 2017; Lv et al., 2017; Bello et al., 2017; Li & Malik, 2016; Metz et al., 2019; 2020).


Algorithms for Advanced Hyper-Parameter Optimization/Tuning - KDnuggets

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Most Professional Machine Learning practitioners follow the ML Pipeline as a standard, to keep their work efficient and to keep the flow of work. A pipeline is created to allow data flow from its raw format to some useful information. All sub-fields in this pipeline's modules are equally important for us to produce quality results, and one of them is Hyper-Parameter Tuning. Most of us know the best way to proceed with Hyper-Parameter Tuning is to use the GridSearchCV or RandomSearchCV from the sklearn module. But apart from these algorithms, there are many other Advanced methods for Hyper-Parameter Tuning.


Curve Fitting With Python

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Curve fitting is a type of optimization that finds an optimal set of parameters for a defined function that best fits a given set of observations. Unlike supervised learning, curve fitting requires that you define the function that maps examples of inputs to outputs. The mapping function, also called the basis function can have any form you like, including a straight line (linear regression), a curved line (polynomial regression), and much more. This provides the flexibility and control to define the form of the curve, where an optimization process is used to find the specific optimal parameters of the function. In this tutorial, you will discover how to perform curve fitting in Python.


Stochastic Hill Climbing in Python from Scratch - DLTK.AI

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Stochastic Hill climbing is an optimization algorithm. It makes use of randomness as part of the search process. This makes the algorithm appropriate for nonlinear objective functions where other local search algorithms do not operate well. It is also a local search algorithm, meaning that it modifies a single solution and searches the relatively local area of the search space until the local optima is located. This means that it is appropriate for unimodal optimization problems or for use after the application of a global optimization algorithm.


Artificial Intelligence can Now Optimize Vibrations of Complex Systems

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The dynamic conduct of a machine device (MT) plays an essential function in fulfilling the principle machining prerequisites, similar to high-speed operations, precision in axis positioning, and the ability to rapidly eliminate a high amount of workpiece material. These exhibitions are legitimately identified with the materials utilized in MT construction. Consequently, materials of MT establishments and moving parts should be chosen with high powerful qualities and the ability to dampen mechanical vibrations. Technical systems are getting progressively complex and simultaneously are turning out to be actually lighter. Considering these difficulties, the vibration optimization of lightweight structures can turn out to be mind-boggling to the point that it presently can don't be constrained by traditional techniques.


Artificial Intelligence can Now Optimize Vibrations of Complex Systems

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The dynamic conduct of a machine device (MT) plays an essential function in fulfilling the principle machining prerequisites, similar to high-speed operations, precision in axis positioning, and the ability to rapidly eliminate a high amount of workpiece material. These exhibitions are legitimately identified with the materials utilized in MT construction. Consequently, materials of MT establishments and moving parts should be chosen with high powerful qualities and the ability to dampen mechanical vibrations. Technical systems are getting progressively complex and simultaneously are turning out to be actually lighter. Considering these difficulties, the vibration optimization of lightweight structures can turn out to be mind-boggling to the point that it presently can don't be constrained by traditional techniques.



Artificial Intelligence: Optimization Algorithms in Python

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What would an "optimal world" look like to you? Would people get along better? Would we take better care of our environment? Many data scientists choose to optimize by using pre-built machine learning libraries. But we think that this kind of'plug-and-play' study hinders your learning. That's why this course gets you to build an optimization algorithm from the ground up.