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Algorithms Articles
Page 11 of 39
Naive Bayes algorithm: Prior, likelihood and marginal likelihood
Introduction Based on Bayes' theorem, the naive Bayes algorithm is a probabilistic classification technique. It is predicated on the idea that a feature's presence in a class is unrelated to the presence of other features. Applications for this technique include text categorization, sentiment analysis, spam filtering, and picture recognition, among many others. A key concept in probability theory, the Bayes theorem provides a method for calculating the likelihood of an event given the chance of related events. Conditional probability, or the possibility of an event happening in the presence of another occurrence, serves as the theoretical foundation. Prior, likelihood and ...
Read MoreFair-share CPU scheduling
Introduction Fair-share CPU scheduling is a scheduling algorithm used in operating systems that aims to allocate CPU resources fairly among different user or process groups. The fair-share scheduler assigns a weight to each process or group based on its historical usage and allocates CPU resources based on these weights, ensuring that no group is starved of resources for an extended period of time. This allows for better resource utilization and provides equal opportunities for all groups to access the CPU. Fair-share scheduling is commonly used in multi-user systems and virtualized environments where multiple users or virtual machines share a single ...
Read MoreDifference between Deep Learning and Reinforcement Learning
Our level of artificial intelligence (AI) maturity, as well as the types of challenges that AI might be able to assist us in resolving, grows in tandem with the ever-increasing volume of data that we produce. This data, along with the incredible computing power that is now available for a price that is affordable, is what is fuels the tremendous growth that has been seen in AI technologies, and it is also what makes deep learning and reinforcement learning possible. In this article, I will explain the difference between Deep Learning and Reinforcement Learning by providing definitions that are clear ...
Read MoreBakery Algorithm in Process Synchronization
It is a prerequisite to understand the terms "process synchronization", "critical section", and "inter-process communication" before we proceed to discuss the Bakery Algorithm in Process Synchronization. What is Process Synchronization? In a multiprocessing system, the process synchronization is a method of coordinating execution of multiple processes so it is ensured that all the processes access shared resources in a controlled and predictable manner. The primary goal of process synchronization is to avoid the problem of race conditions in the system. Though, it also resolves many other issues related to synchronization in a concurrent system. Therefore, the process synchronization ensures that ...
Read MoreEdge Chasing Algorithms
Introduction Edge chasing is a technique used in operating systems and computer hardware to handle events or signals that occur asynchronously with the processor's clock cycle. This technique involves detecting and responding to events or signals as they occur, or as close to their occurrence as possible, to minimize the delay between the event and the system's response. Edge chasing algorithms are used to implement this technique and are an essential component of interrupt handling, input/output operations, and other time-sensitive tasks in modern computer systems. Basic Edge Chasing Algorithms The two basic edge chasing algorithms are polling and interrupts. Polling ...
Read MoreHow changes are Improvement in Modifying an Algorithm?
Introduction Algorithms are essential tools that power the digital world we live in. From search engines to social media platforms, algorithms play a critical role in helping us navigate the vast amount of online information. However, even the most powerful algorithms need updates and modifications to keep up with the ever-evolving digital landscape. In this article, we'll explore how modifying an algorithm can significantly improve efficiency, accuracy, and ability to tackle new challenges. We'll dive into the world of algorithm optimization and explore the different ways in which modifications can transform an algorithm from a reliable tool into an ...
Read MoreBest Visualizations of Machine Learning Algorithms
Introduction Machine learning algorithms are complex and often difficult to interpret and understand. Data visualization can help simplify the complex results generated by these algorithms and make them more accessible to experts and non-experts alike. In this article, we have discussed some of the best visualizations for machine learning algorithms and provided real-time examples. Machine learning algorithms are sophisticated mathematical models that use statistical methods to find patterns in data and generate predictions. Visualizations can shed light on how these algorithms function and the connections they find in the data, even though their inner workings can be challenging to comprehend. ...
Read MoreTop 10 Machine Learning Algorithms For Beginners
Introduction The definition of manual is evolving in a world when almost all manual operations are mechanized. There are many different kinds of machine learning algorithms available today, some of which can help computers learn, get smarter, and resemble humans more. Because technology is advancing rapidly right now, it is possible to anticipate the future by looking at how computers have changed over time. Many different machine learning algorithms have been developed in these extremely dynamic times to aid in resolving difficult real-world problems. The automated, selfcorrecting ML algorithms will get better over time. Let's look at the various sorts ...
Read MoreWhat is an Algorithm in Digital Marketing?
Due to the constantly changing nature of social media algorithms, it can be hard for marketers to keep up with the changes. However, we have a good idea about the algorithm that determines how you rank in social media ads and how you place content on a user's feed. Let’s discuss more social media algorithms and how they work. The main function of social media platforms is to sort and display the most relevant content to each user. This is accomplished by analyzing the massive amount of posts made each day and ranking them based on the interests of ...
Read MoreHow Binary Classification Tree Algorithm Works
Introduction Binary Classification Tree (BCT) is a popular algorithm used in machine learning for supervised learning tasks such as classification. BCT is a type of decision tree algorithm that can be used to classify data into one of two categories (hence the "binary" part of its name). In this article, we will explain how the BCT algorithm works and how it can be used for binary classification tasks. How Binary Classification Tree Algorithm Works The BCT algorithm works by constructing a decision tree based on the features of the input data. The decision tree is a tree-like structure where ...
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