Machine Learning (ML)

Tuning Your AI: The Emergent Field of Prompt Engineering

Prompt engineering is an exciting and rapidly evolving field in the world of artificial intelligence (AI). It is the process of designing, testing, and optimizing inputs, or "prompts," to instruct AI models to generate desired outputs.

Deep Learning

Concept of Multiple Instance Learning (MIL)

In the field of machine learning, Multiple Instance Learning (MIL) is a paradigm that expands upon traditional supervised learning. MIL differs from conventional supervised learning, where each training instance is individually labeled.

Machine Learning (ML)

Feature Selection Problem in Machine Learning

The feature selection problem in machine learning deals with the challenge of identifying the most informative features while eliminating irrelevant or redundant ones. By selecting an effective subset of relevant features.

Machine Learning (ML)

10 Feature Scaling Techniques in Machine Learning

In this article at OpenGenus, we will explore feature scaling techniques in Machine Learning and understand when to use a particular feature scaling technique.

Machine Learning (ML)

Huber and Hinge loss

Loss functions are an important part of Machine Learning. Two common loss functions that we will focus on in this article at OpenGenus are the Huber and Hinge loss functions.

Deep Learning

Predicting Bike Sharing Demand

In this article at OpenGenus, we will work on predicting bike sharing demand using a dataset provided by University of California, Irvine. The dataset contains count of public bikes rented at each hour in Seoul Bike sharing System with the corresponding Weather data and Holidays information.

Python

Different ways to implement Softmax in Python

It is first important to understand what the Softmax function is and what is it used for. Softmax is an important function within neural networks in Machine Learning. A neural network is essentially a method within Deep Learning that acts like a human brain.

Machine Learning (ML)

Implement One-hot encoded array in Python

In this article at OpenGenus, we will learn to implement one-hot encoded array in Python Programming Language using Numpy, Scikit-Learn, Pandas, Keras, TensorFlow and Built-in Python methods.

Machine Learning (ML)

Random Forest Classification

In this article at OpenGenus, we have explained the concept of Random Forest Classification in depth along with model implementation in Python.

Machine Learning (ML)

Decision Tree Classification

In this article at OpenGenus, we have explained the concept of Decision Tree Classification in depth along with model implementation in Python.

Machine Learning (ML)

Extractive vs Abstractive Summarization

In this article at OpenGenus, we have explored the differences between Extractive and Abstractive Summarization in depth and presented the differences in a table.

Machine Learning (ML)

Sparse and Incremental PCA

In this article at OpenGenus, we will explore the concept of Sparse and Incremental PCA.

Deep Learning

21 Time Series/ Forecasting Project Ideas in DL/ML

In this article at OpenGenus, we present a list of 21 time series/forecasting project ideas that can help you improve your skills and understanding of deep learning techniques.

Machine Learning (ML)

Introduction to Ensemble Learning

We have several classifier that classify our data well, but not great, and we had like to combine them into a super classifier that classify our data very well. This discipline is called Ensemble learning.

Deep Learning

Matrix Multiplication vs Dot Product

In this article at OpenGenus, we have explored the importance and link between Matrix Multiplication and Dot Product both in general and in the field of Deep Learning (DL).

Deep Learning

Scaled Dot-Product Attention

There are several approaches to achieve this and this article at OpenGenus aims to walk you through the core operations of one of the most efficient approaches: The Scaled Dot-Product Attention technique.

Machine Learning (ML)

Regression toward the Mean

The goal of this article at OpenGenus is to provide a comprehensive understanding of Regression toward the mean, its applications, potential confounding factors, and how to account for it in experimental designs and machine learning models.

data mining

Forecasting flight delays [Data Mining Project]

The goal of this project at OpenGenus is to use historical data to create a forecasting model for flight delays.

Machine Learning (ML)

Law of Averages

In this article at OpenGenus, we will explore the Law of Averages in Machine Learning.

Machine Learning (ML)

Dynamic Mode Decomposition (DMD): An Overview of the Mathematical Technique and Its Applications

Dynamic Mode Decomposition (DMD) is a data-driven method used to analyze and extract dynamic behavior from high-dimensional data sets. It is a powerful tool for studying fluid dynamics, image processing, and other complex systems.

Deep Learning

Variational Autoencoder with implementation in TensorFlow and Keras

In this article at OpenGenus, we will explore the variational autoencoder, a type of autoencoder along with its implementation using TensorFlow and Keras.

Machine Learning (ML)

Machine Learning for Robotics

This article at OpenGenus aims to shed light on the various applications of Machine Learning in Robotics and some of the fundamental ML concepts used in AI Robots.

Deep Learning

4 Types of backpropagation

In this article at OpenGenus, we have explained the 4 different types of Backpropagation.

Deep Learning

ChatGPT vs Google BARD

Two distinct language models, ChatGPT and Google BARD, were created by two separate businesses, OpenAI and Google, respectively. Even if they have certain things in common, they also differ greatly.

Machine Learning (ML)

Hinge Loss for SVM

This article at OpenGenus will examine the notion of Hinge loss for SVM, providing insight into loss function.