Machine Learning (ML)

Regression dilution

In this article at OpenGenus, we will explore the concept of regression dilution, its implications in various real-world applications. We will discuss when it is crucial to correct for these errors and when it may be appropriate to skip correction.

Demystifying Kernel Density Estimation (KDE) in Python

In this article at OpenGenus, we will start by a general and a mathematical understanding of Kernel Density Estimation and then after exploring some applications of KDE, we, stepwise, implement it in Python.

Deep Learning

ML model to predict Fuel Efficiency [project]

The goal of the fuel efficiency project at OpenGenus is to predict the fuel efficiency of a vehicle based on various input features.

Deep Learning

Neural Scaling Law: A Brief Introduction

Neural scaling law is a term that describes how the performance of a neural network model depends on various factors such as the size of the model, the size of the training dataset, the cost of training, and the complexity of the task.

Machine Learning (ML)

Understanding Bleu Score

BLEU (Bilingual Evaluation Understudy) score was proposed in 2002 and has quickly become the standard score for evaluating machine translation output. It measures the similarity between a machine's translated text and a set of good quality human reference translations.

Machine Learning (ML)

Understanding of Correlation vs. Causation

The ideas of correlation and causation are essential for understanding data and coming to conclusions. This article at OpenGenus will examine the definitions of these phrases, the reasons why they are frequently misinterpreted, and how to tell them apart.

Machine Learning (ML)

Mastering Multi-Label Classification

Dive into the realm of multi-label classification, where AI tackles the intricacies of assigning multiple labels to data points. Explore label correlations, ethical dimensions, and algorithmic strategies in this captivating journey of AI complexity.

Machine Learning (ML)

Non-negative matrix factorization (NMF) vs Principal Component Analysis (PCA)

In the field of data analysis and dimensionality reduction, Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA) are two powerful techniques that play an important role in uncovering patterns, reducing noise, and extracting essential features from complex datasets.

data science

Avoid these 5 Mistakes as a new Data Scientist

In this article at OpenGenus, we have explored 5 common mistakes one make at their first job as a Data Scientist fresh out of School. We wrap up with 5 tips as well.

Deep Learning

Journey through Backpropagation algorithm

This article talks about the process of Backpropagation, an algorithm crucial to refining Neural Networks. It also delves into its core process, explaining how it enables networks to learn from errors and enhance their accuracy. From math to application, witness AI's ongoing enhancement.

Deep Learning

Winograd's Convolution Theorem [Explained]

Winograd Convolution proved a lower bound on the number of multiplications required for convolution, and used Chinese Remainder Theorem to construct optimal algorithm to achieve minimum number of multiplies.

data science

Product based Mock Interview for Data Science

In this article at OpenGenus, we will get familiarized with the flow of a product-based interview round.

Deep Learning

Tensor Operations: Flatten and Squeeze

In this article at OpenGenus, we have explored the 2 fundamental operations in Deep Learning models that modify tensor structure that is Flatten and Squeeze op.

Deep Learning

Adagrad: An Adaptive Gradient Algorithm for Optimization

Adaptive Gradient Algorithm, abbreviated as Adagrad, is a gradient-based optimization algorithm first introduced in 2011.

Machine Learning (ML)

Machine learning mock interview

In this article at OpenGenus, we will see how a machine learning interview for a data science job will be.

Deep Learning

Adam Optimizer

This article introduces the Adam optimizer, an adaptive algorithm widely used in machine learning and deep learning. It combines Adagrad and RMSprop, ensuring faster convergence and improved performance for various tasks like image classification, object detection etc.

Machine Learning (ML)

Brightness and Contrast in Image Processing for ML

Image processing is an important preprocessing step in machine learning because it can greatly improve the quality of the input data, which can in turn improve the performance of machine learning models.

Deep Learning

RMSprop (Root Mean Square Propagation)

RMSprop is a widely used ML optimization algorithm, adapting learning rates based on historical gradients. The article outlines its workings, advantages over traditional methods, discusses some drawbacks, and explores its current applications and future potential.

Artificial Intelligence

Use of AI in Cyber Security: Harnessing the Power of Artificial Intelligence in Cybersecurity

This article at OpenGenus explores the diverse use cases of AI in cybersecurity and highlights specific examples where AI has made a notable impact.

Machine Learning (ML)

Interpretable Machine Learning: Exploring Techniques for Understanding Model Decisions

In this article at OpenGenus, we will delve into various techniques and methods for interpreting machine learning models, empowering practitioners to gain transparency and insights into model decisions.

Deep Learning

Understanding Label Smoothing

In the realm of deep learning, label smoothing has emerged as a powerful technique for improving the generalization and robustness of neural network models.

Machine Learning (ML)

Vehicle Insurance Sales [ML Project]

The research question is what factors influence different types of people to buy auto insurances from a specific company. In this project at OpenGenus, we answer this.

Deep Learning

Global Temperature Change Prediction using ML and DL

This article at OpenGenus explores how ML and DL can be effectively utilized for global temperature change prediction.

Machine Learning (ML)

Interactive Analytics Web Page [ML Project]

In this tutorial at OpenGenus, we will explore how to create an interactive data visualization using D3.js, a popular JavaScript library for data visualization.

Machine Learning (ML)

Identify creditworthy borrowers [ML project]

This article developed during my internship at OpenGenus, we will delve into the key components of credit scoring, including the significance of feature selection, data preprocessing, and model evaluation.