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Articles by Mithilesh Pradhan
Page 3 of 5
Implementation of Particle Swarm Optimization
Introduction The Particle Swarm Optimization algorithm is inspired by nature and is based on the social behavior of birds in a flock or the behavior of fish and is a population-based algorithm for search. It is a simulation to discover the pattern in which birds fly and their formations and grouping during flying activity. Particle Swarm Optimization Algorithm In the PSO Algorithm, each individual is considered to be a particle in some high-dimensional search space. Inspired by the social and psychological behavior of people, which they tend to copy from other people's success, similar changes are made to the particles ...
Read MoreHow to Become an RPA Developer?
Introduction RPA stands for Robotic Process Automation. An RPA developer is a person who designs, maintains, builds, and implements RPA systems. In an organization, an RPA developer has the role to create optimized workflow processes and work cross-functionally with operations and business analysts. Scope of an RPA Developer Today the world is moving towards automation where organizations maximum repetitive processes to be automated as much as possible. Thus the demand for highly skilled RPA professionals has gained pace. With the right skill set, RPA developers can fill the in the respective domains. An RPA developer is essentially a software developer ...
Read MoreExploratory Data Analysis (EDA) - Types and Tools
Introduction Exploratory Data Analysis (EDA) is the process of summarization of a dataset by analyzing it. It is used to investigate a dataset and lay down its characteristics. EDA is a fundamental process in many Data Science or Analysis tasks. Different types of Exploratory Data Analysis There are broadly two categories of EDA Univariate Exploratory Data Analysis – In Univariate Data Analysis we use one variable or feature to determine the characteristics of the dataset. We derive the relationships and distribution of data concerning only one feature or variable. In this category, we have the liberty to use either ...
Read MoreDocument Retrieval using Boolean Model and Vector Space Model
Introduction Document Retrieval in Machine Learning is part of a larger aspect known as Information Retrieval, where a given query by the user, the system tries to find relevant documents to the search query as well as rank them in order of relevance or match. They are different ways of Document retrieval, two popular ones are − Boolean Model Vector Space Model Let us have a brief understanding of each of the above methods. Boolean Model It is a set-based retrieval model.The user query is in boolean form. Queries are joined using AND, OR, NOT, etc. A document ...
Read MoreDeepWalk Algorithm
Introduction The graph is a very useful data structure that can represent co-interactions. These co-interactions can be encoded by neural networks as embeddings to be used in different ML Algorithms. This is where the DeepWalk algorithm shines. In this article, we are going to explore the DeepWalk algorithm with a Word2Vec example. Let us learn more about Graph Networks on which the core of this algorithm is based. The Graph If we consider a particular ecosystem, a graph generally represents the interaction between two or more entities. A Graph Network has two objects – node or vertex and edge. ...
Read MoreExploratory Data Analysis on Iris Dataset
IntroductionIn Machine Learning and Data Science Exploratory Data Analysis is the process of examining a data set and summarizing its main characteristics about it. It may include visual methods to better represent those characteristics or have a general understanding of the dataset. It is a very essential step in a Data Science lifecycle, often consuming a certain time.In this article, we are going to see some of the characteristics of the Iris dataset through Exploratory Data Analysis. The Iris Dataset The Iris Dataset is very simple often referred to as the Hello World. The dataset has 4 features of three ...
Read MoreHow to Calculate Percentiles For Monitoring Data?
Introduction Monitoring online systems, especially which are data intensive is extremely essential for a continuous health check, analyzing and detecting downtimes, and improving performance. The percentile−based method is a very efficient technique to gauge the behavior of such a system. Let's have a look at this method. A General Refresher What are percentiles and why are they useful? In statistics, the value which indicates that below which a certain group of observations falls is called a percentile or centile. For example, for a student, if he/she has scored 90 percentile marks, it means that 90% of the students have scored ...
Read MoreGrowNet: Gradient Boosting Neural Networks
Introduction GrowNet is a novel gradient-boosting framework that uses gradient-boosting techniques to build complex neural networks from shallow deep neural networks. The shallow deep neural networks are used as weak learners. GrowNets today are finding applications in diverse domains and fields. A Brief Refresher of Gradient Boosting Algorithms. Gradient Boosting is the technique to build models sequentially and these models try to reduce the error produced by the previous models. This is done by building a model on the residuals or errors produced by the previous model. It can estimate a function using optimization using numerical methods. The most common ...
Read MoreHandwritten Digit Recognition using Neural Network
Introduction Handwritten Digit Recognition is a part of image recognition widely used in Computer Vision in Deep learning. Image recognition is one of the very basic and preliminary stages of every image or video−related task in Deep Learning. This article lets an overview of Handwritten Digit Recognition and how Image recognition can be extended to multiclass classification. Before going ahead let us understand the difference between Binary and Multiclass image classification Binary Image Classification In Binary image classification, the model has two classes to predict from. For example in the classification of cats and dogs. Multiclass Image Classification In Multiclass ...
Read MoreSimultaneous Localization and Mapping
Introduction Simultaneous Localization and Mapping or SLAM is a method that let us build a map and locate our vehicles on that map at the same time. SLAM algorithms are used for unknown environment mapping and simultaneous localization. How is SLAM useful? Engineers can use SLAM for avoiding obstacles and also use them for path planning. SLAM software allows robot systems, drones, or autonomous vehicles to find paths in unknown environments and difficult terrains. This process involves a high amount of computing and processing power. SLAM can be useful for mapping areas that are too small or dangerous for ...
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