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Algorithms Articles
Page 2 of 39
CSMA 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 ...
Read MoreUnderstanding Sparse Transformer: Stride and Fixed Factorized Attention
Transformer models have progressed much in natural language processing (NLP), getting state-of-the-art results in many tasks. But Transformers' computational complexity and memory needs increase by a factor of four with the length of the input sequence. This makes it hard to handle long sequences quickly. Researchers have developed Sparse Transformers, an extension of the Transformer design that adds sparse attention mechanisms, to get around these problems. This article looks at the idea of Sparse Transformers, with a focus on Stride and Fixed Factorized Attention, two methods that help make these models more efficient and effective. Transformer Recap Before getting into ...
Read MoreUnderstanding AHA: Artificial Hippocampal Algorithm
Introduction The brain is the most complicated organ and is used for various scientific studies. The human brain is studied and the prototype is implemented for artificial intelligence (AI) and machine learning (ML). The hippocampus is an essential part of the brain. It helps us learn, remember, and find our way around. Researchers have tried to create an Artificial Hippocampus Algorithm (AHA) that can copy the functions and skills of the hippocampus in ML systems. This article discusses AHA, its mechanisms, scopes, and limitations. Motivation for Artificial Hippocampus Algorithm The goal of making an AHA is to improve the ability ...
Read MoreHow to Explain Steady State Genetic Algorithm (SSGA) in Machine Learning?
Steady State Genetic Algorithm (SSGA) is often used in machine learning and optimization tasks. It is a population-based, iterative search method based on the ideas behind natural evolution and genetics. SSGA works with a group of possible answers, shown as people or chromosomes. Here's how SSGA genetic Algorithm works Initialization − The algorithm starts by making a group called the starting population. Each person is a possible way to solve the problem at hand. Most of the time, the population is made or started randomly based on what we already know about the problem area. Evaluation − Everyone in ...
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