Machine Learning (ML)

Active Learning: How Machines Learn Better with Human Guidance

Active learning solves this problem by letting the model query a human expert for labels on the most uncertain or informative data points.

Stepwise Regression

We will be learning about stepwise regression- a technique that will help us find the best set of variables to choose for our linear regression.

Deep Learning

Guide to Deep Learning Model Training and Quantization

In this OpenGenus article, I will be guiding you through training a sample convolutional neural network (ConvNet) with 5 convolutional layers for a specific task.

Deep Learning

INT4 Quantization (with code demonstration)

INT4 quantization is a technique used to optimize deep learning models by reducing their size and computational costs. It achieves this by using 4-bit integers instead of 32-bit floating-point numbers.

Machine Learning (ML)

Personality Prediction Through Machine Learning

A person's action or reaction to any issue is largely dependent on the answer to the question: What kind of a person he is? In this OpenGenus article, we aim to create a Machine Learning model which can tell us exactly that.

Machine Learning (ML)

Genre Classification through Song Lyrics

This article presents a practical implementation of music genre classification using song lyrics and machine learning algorithms. By leveraging NLP techniques and logistic regression, it demonstrates efficient categorization of songs, offering insights for music analysis and recommendation systems.

List of Mathematical Algorithms

Causal Inference

The process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system is called Causal inference. Usual statistical methods like correlation does not ensures causality. That's why we need a more scientific method to ensure causation.

Machine Learning (ML)

Markov Chain Neural Network (MCNN)

In this OpenGenus article, we will explore Markov chains, their intersections with neural networks, and a simple implementation of a Markov chain neural network (MCNN). We will also take a look at their applications.

Deep Learning

Basics of using PyTorch

PyTorch is an open-source deep learning framework primarily developed by Facebook's AI Research lab (FAIR).

data science

Everything about Sample Size

Sample size refers to the number of individual observations or data points collected from a population for a specific study or analysis.

Machine Learning (ML)

Cluster Sampling

Cluster sampling is a probabilistic sampling approach wherein the population is divided into clusters, and a random subset of clusters is chosen for examination.

Machine Learning (ML)

Why convexity is important in Machine Learning?

In machine learning, convexity plays a crucial role in optimization problems. Convex optimization problems are desirable because they ensure the existence of a unique global minimum.

System Design

Vector Databases: Long-term Memory of LLMs

In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have become the cornerstone of various applications, from natural language processing to content generation.

Machine Learning (ML)

Sentiment Analysis For Mental Health Sites and Forums

This OpenGenus article delves into the crucial role of sentiment analysis in understanding emotions on mental health platforms. Featuring a Python program using NLTK's VADER, it explains the importance of comprehending user emotions for early intervention and personalized user experiences.

Deep Learning

Introduction to Multi-Agent Systems (MAS)

Multi-Agent Systems (MAS) consist of autonomous agents that interact with each other and their environment to achieve individual or collective goals.

Machine Learning (ML)

ML model to predict Waiter’s Tip

This OpenGenus article explores the application of machine learning techniques in predicting waiter tips based on factors such as total bill, day and time. Written in Python, the code employs one-hot encoding and Linear Regression, offering an understanding of these concepts.

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.

Machine Learning (ML)

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.