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Articles by Pranavnath
Page 31 of 39
Basis Vectors in Linear Algebra in Machine Learning
Introduction Linear algebra forms the backbone of many machine learning algorithms, and one key concept within this field is that of basis vectors. In machine learning, basis vectors provide a powerful framework for representing and understanding complex data sets. By decomposing data into its constituents based on these vectors, we unlock new ways to extract meaningful patterns and make accurate predictions. This article explores the role of basis vectors in linear algebra's application to machine learning. Understanding how to leverage basis vectors empowers researchers and practitioners to push the boundaries of machine learning, ultimately leading us towards smarter technologies ...
Read MoreWeighted Product Method in Multi Criteria Decision Making
Introduction Within the domain of decision−making, there are often multiple criteria that have to be considered at the same time. Whether it's selecting an unused item to dispatch, choosing a venture procedure, or making an individual choice, assessing and positioning choices based on different components can be a complex assignment. Multi−Criteria Decision Making (MCDM) strategies offer an organized approach to handling such issues. One such strategy is the Weighted Product Method (WPM), which gives an orderly way of consolidating preferences and weighting criteria to reach at the last choice. In this article, we'll dig into the complexities of the Weighted ...
Read MoreDeep Neural Net with forward and Back Propagation
Introduction Artificial intelligence and machine learning have experienced a transformation since to Deep Neural Networks (DNN), which have empowered exceptional progressions over a assortment of areas. In this article, we'll look at the thoughts of forward and backward propagation and how they relate to the advancement and advancement of advanced neural systems. Python librariеs likе TеnsorFlow havе incredibly streamlined thе execution of thе systеms, making thеm morе opеn to analysts and professionals. Approach 1 : Tensorflow In this approach, we utilize the control of the TensorFlow library to execute a profound neural arrange with forward and backpropagation. ...
Read MoreThe Optimal Number of Epochs to Train a Neural Network in Keras
Introduction Training a neural network includes finding the proper adjustment between under fitting and overfitting. In this article, we'll learn the epochs’s concept and dive into deciding the epoch’s number, a well−known deep−learning library. By understanding the trade−off between underfitting and overfitting, utilizing methods like early ceasing and cross−validation, and considering learning curves, we are able successfully to decide the perfect number of epochs. Understanding Epochs An epoch alludes to one total pass of the whole preparing dataset through a neural network. Amid each epoch, the network learns from the training information and updates its internal parameters, such as ...
Read MoreUsing Interquartile Range to Detect Outliers in Data
Introduction Data analysis plays a significant part in different areas, counting commerce, back, healthcare, and investigation. One common challenge in data analysis is the nearness of outliers, which are data focuses that essentially deviate from the overall design of the data. These outliers can distort statistical measures and influence the exactness of our examination. Hence, it gets to be imperative to distinguish and handle outliers appropriately. In this article, the user will understand the concept of IQR and its application in identifying outliers in data. Python Program to Detect Outliers Algorithm Step 1 :Calculate the mean and deviation of the ...
Read MoreVisual representations of Outputs/Activations of each CNN layer
Introduction Convolutional neural networks offer remarkable insight into mimicking human−like visual processing through their sophisticated multi−layer architectures. This article has taken you on a creative journey through each layer's function and provided visual representations of their outputs or activations along the way. As researchers continue to unlock even deeper levels of understanding within CNNs, we move closer toward unraveling the mysteries behind complex intelligence exhibited by these futuristic machines. In this article, we embark on a fascinating journey through the layers of CNNs to unravel how these remarkable machines work. Visual representation of Outputs The Input Layer − Where ...
Read MorePerceptron Algorithm for NOT Logic Gate
Introduction Within the domain of artificial intelligence and machine learning, the Perceptron Algorithm holds a special put as one of the foundational building blocks. Although it could seem basic in comparison to present−day complex neural networks, understanding the Perceptron Algorithm is basic because it shapes the premise for many modern learning techniques. In this article, we are going to investigate the Perceptron Algorithm with a center on its application to the NOT logic gate. We are going to dig into the hypothesis behind the algorithm, its components, and how it can be used to implement the logical NOT operation. ...
Read MorePerceptron Algorithm for NAND Logic Gate with 2-bit Binary Input
Introduction Within the domain of Artificial Intelligence and Machine Learning, one of the foremost basic components is the Artificial Neural Network (ANN). ANNs are motivated by the human brain's neural systems and are designed to imitate the way neurons prepare data. At the center of an ANN lies the perceptron, an essential building square that serves as a basic numerical model of a neuron. In this article, we'll investigate the Perceptron NAND Logic Gate with 2−bit Binary Input, and basic however fundamental concept within the world of ANNs. Understanding the Perceptron The perceptron, proposed by Frank Rosenblatt in 1957, could ...
Read MoreWhat is Residual Networks(ResNet) in Deep Learning
Introduction Deep learning has revolutionized the field of artificial intelligence, empowering the advancement of profoundly precise and effective models for different errands such as picture classification, protest location, and normal dialect handling. One critical headway in profound learning designs is the presentation of Leftover Systems, commonly known as ResNet. ResNet has accomplished exceptional execution in picture acknowledgment assignments, outperforming the capabilities of past convolutional neural network (CNN) designs. In this article, we'll investigate the concept of Residual networks (ResNet) and get why they have ended up being a game−changer in profound learning. What is Residual Network (ResNet)? ...
Read MoreUnivariate Optimization vs Multivariate Optimization
Introduction In this article, we will explore the differences between these approaches and analyze their advantages and limitations. Both univariate and multivariate optimization approaches have distinct strengths and limitations for different applications. Optimization is a tool which would be utilize to retrieve the best solution. Multivariate optimization aims to find the optimal combination of variables that will result in the best possible solution. Univariate Optimization vs Multivariate Optimization Univariate Optimization Univariate optimization involves finding an optimal value for a single−variable problem within a given range. This method seeks to maximize or minimize an objective function by iteratively evaluating different values ...
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