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Articles by Premansh Sharma
Page 3 of 7
How to Increase Classification Model Accuracy?
Introduction Machine learning largely relies on classification models, and the accuracy of these models is a key performance indicator. It can be difficult to increase a classification model's accuracy since it depends on a number of variables, including data quality, model complexity, hyperparameters, and others. In this post, we'll look at a few methods for improving a classification model's precision. Ways to Increase Accuracy Data Preprocessing Each machine learning project must include data preprocessing since the model's performance may be greatly impacted by the quality of the training data. There are various processes in ...
Read MoreBuilding a Fraud Detection Model for a Bank
Introduction Financial fraud has become an increasingly common problem for banks and financial organizations throughout the world as technology advances. Money laundering, identity theft, and credit card fraud can all result in major financial losses as well as damage to a bank's image. As a result, banks must take proactive steps to prevent and detect fraudulent activity. Building a fraud detection model is one such method that can assist identify fraudulent transactions and flag them for further examination. In this article, we will examine the steps involved in creating a fraud detection model for a bank, starting with ...
Read MoreHow to Train MFCC Using Machine Learning Algorithms
Introduction Mel Frequency Cepstral Coefficients (MFCCs) is a widely used feature extraction technique for audio processing, particularly in speech recognition applications. A logarithmic compression, a filter bank, and the discrete Fourier transform (DFT) of audio signals in brief time intervals are used to create MFCCs. You will have a thorough understanding of how to train MFCC using machine learning algorithms by the end of this article. What is an MFCC MFCC stands for Mel−Frequency Cepstral Coefficients. It is a widely used feature extraction technique in audio signal processing and speech recognition. The MFCC algorithm is based on the human ...
Read MoreGeorgia Tech MS Degree in CS(Machine Learning) vs. NYU MS Degree in Data Science
Introduction Data science and machine learning are fast expanding professions, and having a graduate degree in these topics might provide you an advantage in the employment market. Yet, with so many applications accessible, it might be difficult to select the best one. The MS degree in CS (Machine Learning) from Georgia Tech and the MS degree in Data Science from NYU are two prominent possibilities. The curriculum at Georgia Tech is strongly focused on computer science and machine learning techniques and systems. The curriculum at NYU is more multidisciplinary, covering areas like as statistics, machine learning, data visualisation, and data ...
Read MoreRidge and Lasso Regression Explained
Introduction Two well-liked regularization methods for linear regression models are ridge and lasso regression. They help to solve the overfitting issue, which arises when a model is overly complicated and fits the training data too well, leading to worse performance on fresh data. Ridge regression reduces the size of the coefficients and prevents overfitting by introducing a penalty element to the cost function of linear regression. The squared coefficient total is directly proportional to this penalty component. Adversely, a penalty term is added in lasso regression that is proportionate to the total of the absolute values of the coefficients. This ...
Read MoreNaive Bayes algorithm: Prior, likelihood and marginal likelihood
Introduction Based on Bayes' theorem, the naive Bayes algorithm is a probabilistic classification technique. It is predicated on the idea that a feature's presence in a class is unrelated to the presence of other features. Applications for this technique include text categorization, sentiment analysis, spam filtering, and picture recognition, among many others. A key concept in probability theory, the Bayes theorem provides a method for calculating the likelihood of an event given the chance of related events. Conditional probability, or the possibility of an event happening in the presence of another occurrence, serves as the theoretical foundation. Prior, likelihood and ...
Read MoreWhat is learning rate in Neural Networks?
In neural network models, the learning rate is a crucial hyperparameter that regulates the magnitude of weight updates applied during training. It is crucial in influencing the rate of convergence and the caliber of a model's answer. To make sure the model is learning properly without overshooting or converging too slowly, an adequate learning rate must be chosen. The notion of learning rate in neural networks, its significance, and numerous methods to choose an optimal learning rate will all be covered in this article. We will also go through how to identify and resolve typical learning rate issues that develop ...
Read MoreFixing constant validation accuracy in CNN model training
Introduction The categorization of images and the identification of objects are two computer vision tasks that frequently employ convolutional neural networks (CNNs). Yet, it can be difficult to train a CNN model, particularly if the validation accuracy approaches a plateau and stays that way for a long time. Several factors, including insufficient training data, poor hyperparameter tuning, model complexity, and overfitting, might contribute to this problem. In this post, we'll talk about a few tried-and-true methods for improving constant validation accuracy in CNN training. These methods involve data augmentation, learning rate adjustment, batch size tuning, regularization, optimizer selection, initialization, and ...
Read MoreActivation Function in a Neural Network: Sigmoid vs Tanh
Introduction Due to the non-linearity that can introduce towards the output of neurons, activation functions are essential to the functioning of neural networks. Sigmoid and tanh are two of the most often employed activation functions in neural networks. Binary classification issues frequently employ the sigmoid function in the output layer to transfer input values to a range between 0 and 1. In the deep layers of neural networks, the tanh function, which translates input values to a range between -1 and 1, is frequently applied. The usage of either function relies on the particular needs of the issue being handled ...
Read MoreCNN vs ANN for Image Classification
Introduction There has been a lot of interest in creating efficient machine-learning models for picture categorization due to its growing significance in several industries, including security, autonomous driving, and healthcare. Artificial neural networks (ANNs) and convolutional neural networks (CNNs) are two common models for classifying images. While both CNNs and ANNs can perform image classification tasks with high accuracy, their architectural designs and learning methods vary. ANN vs CNN Identifying the elements or objects in a picture is the process of image classification. It is a key job in computer vision, having uses in anything from autonomous vehicles to ...
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