Locally Weighted Linear Regression is a non−parametric method/algorithm. In Linear regression, the data should be distributed linearly whereas Locally Weighted Regression is suitable for non−linearly distributed data. Generally, in Locally Weighted Regression, points closer to the query point are given more weightage than points away from it. Parametric and Non-Parametric Models Parametric Parametric models are those which simplify the function to a known form. It has a collection of parameters that summarize the data through these parameters. These parameters are fixed in number, which means that the model already knows about these parameters and they do not depend on the ... Read More

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 More

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 More

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 More

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 More

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 More

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 More

Neural networks and deep learning systems are useful for a number of tasks, including pattern recognition and classification. These methods can be used to analyze large and complex datasets, and can often achieve high levels of accuracy in tasks that are difficult for traditional algorithms to solve. Additionally, neural networks and deep learning systems are able to learn and improve over time, which makes them particularly well−suited for tasks that involve unstructured or unlabeled data. In this article, we'll examine neural networks and deep learning systems in-depth and discuss how they vary from one another. What is Neural Network? A ... Read More

In natural language processing, the interlingua and transfer techniques are employed to facilitate language translation and other language-related activities. These techniques are valuable because they enable automatic text translation from one language to another, which may be beneficial in a number of scenarios such as international communication or the processing of vast volumes of multilingual text data. In this post, we will examine and contrast the Interlingua Approach with the Transfer Approach. What is the Interlingua Approach? The interlingua approach is a method for translating text from one language to another in natural language processing. Its foundation is the idea ... Read More

Machine learning projects employ machine learning algorithms and techniques to create models that can make predictions or judgments based on input data. These projects frequently include building a machine learning model on a big dataset, followed by utilizing the taught model to make predictions or choices on fresh, previously unknown data. Machine learning projects can be classified into three types: supervised learning, unsupervised learning, and reinforcement learning. The model is trained on labeled data in supervised learning, and the proper output is delivered for each example in the training set. In unsupervised learning, the model is not given with labeled ... Read More