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Algorithms Articles
Found 386 articles
Activity Selection Problem
Activity Selection Problem The activity selection problem is an example of a greedy algorithm where the maximum number of non-overlapping activities are selected from the given activity set. A person can complete one activity at a time. The activities are given in the form of their starting and completion times. In this article, we have an array of integers that stores the starting and completion time of each activity. Our task is to select the maximum number of non-overlapping activities from the given activity array. Scenario An example of the maximum activity ...
Read MorePartitioning Method (K-Mean) in Data Mining
The present article breaks down the concept of K-Means, a prevalent partitioning method, from its algorithmic framework to its pros and cons, helping you better grasp this sophisticated tool. Let's dive into the captivating world of K-Means clustering! K-Means Algorithm The K-Means algorithm is a centroid-based technique commonly used in data mining and clustering analysis. How K-Means Works? The K-Means Algorithm, a principle player in partitioning methods of data mining, operates through a series of clear steps that move from basic data grouping to detailed cluster analysis. Initialization − Specify the number of clusters 'K' to be created. This ...
Read MoreAlgorithms and Complexities
AlgorithmAn algorithm is a finite set of instructions, those if followed, accomplishes a particular task. It is not language specific, we can use any language and symbols to represent instructions.The criteria of an algorithmInput: Zero or more inputs are externally supplied to the algorithm.Output: At least one output is produced by an algorithm.Definiteness: Each instruction is clear and unambiguous.Finiteness: In an algorithm, it will be terminated after a finite number of steps for all different cases.Effectiveness: Each instruction must be very basic, so the purpose of those instructions must be very clear to us.Analysis of algorithmsAlgorithm analysis is an important part ...
Read MoreCSMA with Collision Detection (CSMA/CD
Carrier Sense Multiple Access with Collision Detection (CSMA/CD) is a network protocol for carrier transmission that operates in the Medium Access Control (MAC) layer. It senses or listens whether the shared channel for transmission is busy or not, and defers transmissions until the channel is free. The collision detection technology detects collisions by sensing transmissions from other stations. On detection of a collision, the station stops transmitting, sends a jam signal, and then waits for a random time interval before retransmission.AlgorithmsThe algorithm of CSMA/CD is:When a frame is ready, the transmitting station checks whether the channel is idle or busy.If ...
Read MoreDifference between BFS and DFS
Both BFS and DFS are types of graph traversal algorithms, but they are different from each other. BFS or Breadth First Search starts from the top node in the graph and travels down until it reaches the root node. On the other hand, DFS or Depth First Search starts from the top node and follows a path to reaches the end node of the path. Read this article to learn more about these two graph traversal algorithms and how they are different from each other. What is BFS? Breadth First Search (BFS) algorithm traverses a graph in a breadth-ward motion ...
Read MoreDifference Between Algorithm and Pseudocode
Algorithm and Pseudocode are the two related terms in computer programming. The basic difference between algorithm and pseudocode is that an algorithm is a step-by-step procedure developed to solve a problem, while a pseudocode is a technique of developing an algorithm. In this article, we will discuss the other important differences between an algorithm and a pseudocode. Let's start with some basic concepts of algorithm and pseudocode. What is an Algorithm? A sequence of steps to solve a given problem is called as algorithm. Thus, an algorithm is a step-by-step procedure developed for solving a given problem. An ...
Read MoreWhat is Grouped Convolution in Machine Learning?
Introduction The idea of filter groups, also known as grouped convolution, was first explored by AlexNet in 2012. This creative solution was prompted by the necessity to train the network using two Nvidia GTX 580 GPUs with 1.5GB of memory each. Challenge: Limited GPU Memory During testing, AlexNet's creators discovered it needed a little under 3GB of GPU RAM to train. Unfortunately, they couldn't train the model effectively using both GPUs because of memory limitations. The Motivation behind Filter Groups In order to solve the GPU memory problem, the authors came up with filter groups. By optimizing the model's parallelization ...
Read MoreHow does Short Term Memory in machine learning work?
Introduction LSTM, which stands for Long Short-Term Memory, is an advanced form of recurrent neural network (RNN) specifically designed to analyze sequential data like text, speech, and time series. Unlike conventional RNNs, which struggle to capture long-term dependencies in data, LSTMs excel in understanding and predicting patterns within sequences. Conventional RNNs face a significant challenge in retaining crucial information as they process sequences over time. This limitation hampers their ability to make accurate predictions based on long-term memory. LSTM was developed to overcome this hurdle by enabling the network to store and maintain information for extended periods. Structure of an ...
Read MoreEpisodic Memory and Deep Q-Networks in machine learning explained
Introduction In recent years, deep neural networks (DNN) have made significant progress in reinforcement learning algorithms. In order to achieve desirable results, these algorithms, however, suffer from sample inefficiency. A promising approach to tackling this challenge is episodic memory-based reinforcement learning, which enables agents to grasp optimal actions rapidly. Using episodic memory to enhance agent training, Episodic Memory Deep Q-Networks (EMDQN) are a biologically inspired RL algorithm. Research shows that EMDQN significantly improves sample efficiency, thereby improving the chances of discovering effective policies. It surpasses both regular DQN and other episodic memory-based RL algorithms by achieving state-of-the-art performance on Atari ...
Read MoreGuide to probability Density Estimation & Maximum Likelihood Estimation
Density Estimation is an essential part of both machine learning and statistics. It means getting the probability density function (PDF) of a group. It is necessary for many things, like finding outliers, putting things into groups, making models, and finding problems. Based on deep learning, this study looks at all the ways to measure old and new density. Traditional Density Estimation Methods Histograms Whether you need to know in a hurry whether your data collection is complete, a histogram is the way to go. They take the data range and chunk it up into categories called " bins " to determine ...
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