Machine Learning (ML)

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

Squeeze and Excitation (SE) Block

The SE block focuses specifically on improving the channel relationship. It introduces a mechanism to capture and emphasize important channel-wise information, enabling CNNs to better discriminate and learn relevant features.

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.

Algorithms

Fast Fourier Transformation and its Application in Polynomial Multiplication

The Fast Fourier Transformation (FFT) is an algorithm for computing the discrete Fourier transform (DFT) of a sequence. The DFT is a mathematical operation that converts a sequence of numbers into a sequence of frequencies.

Machine Learning (ML)

Predicting Customer Lifetime Value in Retail [ML Project]

The process of calculating the Lifetime Value is relatively straightforward. First, we need to determine a specific time window, which can range from 3 to 24 months.

Machine Learning (ML)

Hyperplane in SVM

In SVMs, a hyperplane is a subspace of one dimension less than the original feature space. In two-dimensional space, a hyperplane is a line, while in three-dimensional space, it is a plane.

Web Development

16 Uses of Machine Learning (ML, DL, AI) in Web Development

In this article at OpenGenus, we will talk about the use of Machine Learning, Deep Learning and Artificial Intelligence in web development.

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.