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Optimization


Linear Programming for Data Science and Business Analysis

#artificialintelligence

In this course you will learn all about the mathematical optimization of linear programming for data science and business analytics. This course is very unique and have its own importance in their respective disciplines. The data science and business study heavily rely on optimization. Optimization is the study of analysis and interpreting mathematical data under the special rules and formula. The length of the course is more than 6 hours and there are total more than 4 sections in this course.


Free book – for #datascience interviews - Guide to competitive programming

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Recently Springer made some good books on maths free to download. Competitive programming strategies are useful for many data science interviews and they help to improve your maths foundations. There are not many books on this subject (although there are many good websites and YouTube resources).


Various Optimization Algorithms For Training Neural Network

#artificialintelligence

Many people may be using optimizers while training the neural network without knowing that the method is known as optimization. Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. How you should change your weights or learning rates of your neural network to reduce the losses is defined by the optimizers you use. Optimization algorithms or strategies are responsible for reducing the losses and to provide the most accurate results possible. We'll learn about different types of optimizers and their advantages: Gradient Descent is the most basic but most used optimization algorithm.


Overview of various Optimizers in Neural Networks

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Optimizers are algorithms or methods used to change the attributes of the neural network such as weights and learning rate to reduce the losses. Optimizers are used to solve optimization problems by minimizing the function. How you should change your weights or learning rates of your neural network to reduce the losses is defined by the optimizers you use. Optimization algorithms are responsible for reducing the losses and to provide the most accurate results possible. The weight is initialized using some initialization strategies and is updated with each epoch according to the update equation. The above equation is the update equation using which weights are updated to reach the most accurate result.


5 Algorithms that Changed the World

#artificialintelligence

An algorithm is an unambiguous rule of action to solve a problem or a class of problems. Algorithms consist of a finite number of well-defined individual steps. Thus, they can be implemented in a computer program for execution, but can also be formulated in human language. When solving a problem, a specific input is converted into a particular output. In the following, five algorithms are listed that have significantly influenced our world.


Quantum computing: A key ally for meeting business objectives

MIT Technology Review

In the business world, the opportunities for applying quantum technology relate to optimization: solving difficult business problems, reconfiguring complex processes, and understanding correlations between seemingly disparate data sets. The main purpose of quantum computing is to carry out computationally costly operations in a very short period of time, while at the same time accelerating business performance. Quantum computing can optimize business processes for any number of solutions, for example maximizing cost/benefit ratios or optimizing financial assets, operations and logistics, and workforce management--usually delivering immediate financial gains. Many businesses are already using (or planning to use) classic optimization algorithms. And with four international case studies, Reply has proven that a quantum approach can give better results than existing optimization techniques. Speed and computational power are key components when working with data.


Fine-Tuning ML Hyperparameters

#artificialintelligence

"Just as electricity transformed almost every industry 100 years ago, today I actually have hard time thinking of an industry that I don't think AI (Artificial Intelligence) will transform in the next several years" -- Andrew NG I have long been fascinated with these algorithms, capable of something that we can as humans barely begin to comprehend. However, even with all these resources one of the biggest setbacks any ML practitioner has ever faced would be tuning the model's hyperparameters. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. The same kind of machine learning model can be trained on different constraints, learning rates or kernels and other such parameters to generalize to different datasets, and hence these instructions have to be tuned so that the model can optimally solve the machine learning problem.



The right Loss Function? [PyTorch]

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Loss Functions are one of the most important parts of Neural Network design. A loss function helps us to interact with the model and tell the model what we want -- the reason why it is related to an "objective function". Let us look at the precise definition of a loss function. In mathematical optimization and decision theory, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function.


From Understanding Genetic Drift to a Smart-Restart Parameter-less Compact Genetic Algorithm

arXiv.org Artificial Intelligence

One of the key difficulties in using estimation-of-distribution algorithms is choosing the population sizes appropriately: Too small values lead to genetic drift, which can cause enormous difficulties. In the regime with no genetic drift, however, often the runtime is roughly proportional to the population size, which renders large population sizes inefficient. Based on a recent quantitative analysis which population sizes lead to genetic drift, we propose a parameter-less version of the compact genetic algorithm that automatically finds a suitable population size without spending too much time in situations unfavorable due to genetic drift. We prove an easy mathematical runtime guarantee for this algorithm and conduct an extensive experimental analysis on four classic benchmark problems. The former shows that under a natural assumption, our algorithm has a performance similar to the one obtainable from the best population size. The latter confirms that missing the right population size can be highly detrimental and shows that our algorithm as well as a previously proposed parameter-less one based on parallel runs avoids such pitfalls. Comparing the two approaches, ours profits from its ability to abort runs which are likely to be stuck in a genetic drift situation.