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Articles by Mithilesh Pradhan
Page 4 of 5
Exploratory 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 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 ...
Read MoreRole of Log Odds in Logistic Regression
Introduction Logistic Regression is a statistical method to predict a dependent data variable based on the relationship between one or more independent variables. It makes use of log odds and with the help of a logistic function, it predicts the probability of an event occurring. It is a classification method. What are Log Odds and Why are they Useful for Logistic Regression? Logistic regression is used to predict binary outcomes. For example, in an election, whether a candidate will win or not, whether SMS is spam or ham, etc. Odds are the ratio of the probability of success to failure. ...
Read MoreImplementation of Whale Optimization Algorithm
Introduction Whale Optimization Algorithm is a technique for solving optimization problems in Mathematics and Machine Learning. It is based on the behavior of humpback whales which uses operators like prey searching, encircling the prey, and forging bubble net behavior of humpback whales in the ocean. It was given by Mirjalili and Lewis in 2016. In this article, we are going to look into the different phases of the WOA algorithm A History of Humpback Whales Humpback whales are one of the largest mammals on Earth. They have a special type of hunting mechanism known as the bubble−net hunting mechanism. They ...
Read MoreImage Recognition using MobileNet
Introduction The process of identifying an object or feature with an image is known as Image Recognition. Image recognition finds its place in diverse domains be it Medical imaging, automobiles, security, or detecting defects. What is MobileNet and Why is it so Popular? MobileNet is deep learning CNN model developed using depth−wise separable convolutions. This model highly decreases the number of parameters when compared to other models of the same depth. This model is lightweight and is optimized to run on mobile and edge devices. There are three versions of Mobilenet released so far.ie MobileNet v1, v2 and v3. Mobilenet ...
Read MoreHow to Improve UX With Machine Learning?
Introduction User experience (UX) is how a person or user interacts with a product, service, or system encompassing everything from ease of usage, and its usefulness to efficiency. Today, Machine Learning can provide an intuitive user experience through modeling, customization, clustering, and segregation. In this article, let's have a look at how Machine Learning is revolutionizing User Experience. Why does User Experience Matters? In the case of a business that needs to attract customers or to make sales via a website or mobile app UX is almost needed. The duration of time the user spends on these platforms, their search ...
Read MoreTraining vs Testing vs Validation Sets
In this article, we are going to learn about the difference between – Training, Testing, and Validation sets Introduction Data splitting is one of the simplest preprocessing techniques we can use in a Machine Learning/Deep Learning task. The original dataset is split into subsets like training, test, and validation sets. One of the prime reasons this is done is to tackle the problem of overfitting. However, there are other benefits as well. Let's have a brief understanding of these terms and see how they are useful. Training Set The training set is used to fit or train the model. These ...
Read MoreTop 7 Artificial Intelligence and Machine Learning Trends For 2022
In this article, let’s explore 7 such areas which are promising and booming in 2022 related to Artificial Intelligence. Introduction Over the last decade, we saw tremendous growth and development in Artificial Intelligence-related technologies in almost every domain and is still becoming more relevant in the current era or decade. There have been ground-breaking research in AI and ML which has changed the current scenario of how we pursue technology infused with machine intelligence.AI related technology has found application in every domain. To name a few like Healthcare, Cybersecurity, HR Processes, Space exploration, and many more. Trends in 2022 1. ...
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