Samyak Deshpande - OpenGenus IQ: Computing Expertise & Legacy

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.