Samyak Deshpande - OpenGenus IQ: Computing Expertise & Legacy

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)

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.

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.

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.

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)

Fairness in Machine Learning

Fairness in machine learning is an increasingly important topic as machine learning models are being used in a wide range of applications, from lending and hiring decisions to criminal justice systems.

Machine Learning (ML)

Decision Boundary in ML

Decision boundary is a crucial concept in machine learning and pattern recognition. It refers to the boundary or surface that separates different classes or categories in a classification problem.

Machine Learning (ML)

Gibbs Sampling

Gibbs Sampling is a statistical method for obtaining a sequence of samples from a multivariate probability distribution.

Machine Learning (ML)

Feature, vector and embedding space

In this article, we will discuss the concepts of feature, vector, and embedding space and their importance in machine learning.

Deep Learning

Training, Testing, Validation and Holdout Set

Training, testing, validation, and holdout sets are essential components of machine learning models that allow for effective evaluation of model performance and generalization. In this article, we will delve into what these sets are, how they are used, and why they are important.

Deep Learning

Epoch, Iteration and Batch in Deep Learning

In this article, we will explore three fundamental concepts in deep learning: epoch, iteration, and batch. These concepts are essential in training deep neural networks and improving their accuracy and performance.

Machine Learning (ML)

Large Counts Condition and Large Enough Sample Rule

Large Counts Condition and Large Enough Sample Rule are two important concepts in the fields of machine learning and statistics that are used to make inferences about populations based on samples.

C++

4 ways to remove elements from Multiset in C++ STL

Multiset is implemented as a balanced binary search tree. Removing elements from a multiset is an important operation in many algorithms and applications. In this article, we will explore different ways to remove elements from a multiset in C++ STL.