Linear mapping Let V and W are the vector spaces over field K. A function f: V-> W is said to be the linear map… Read More

# Category Archives: Machine Learning

Before jumping into the term “Data Analysis”, let’s discuss the term “Analysis”. Analysis in Layman’s language (Plain English) is a process of answering “How?” and… Read More

In linear or multiple regression, it is not enough to just fit the model into the dataset. But, it may not give the desired result.… Read More

Beta Variational Autoencoders was proposed by researchers at Deepmind in 2017. It was accepted in the International Conference on Learning Representations (ICLR) 2017. Before learning… Read More

The goal of contrastive learning is to learn such embedding space in which similar samples are close to each other while dissimilar ones are far… Read More

True Error The true error can be said as the probability that the hypothesis will misclassify a single randomly drawn sample from the population. Here… Read More

The Singular Value Decomposition (SVD) of a matrix is a factorization of that matrix into three matrices. It has some interesting algebraic properties and conveys… Read More

Affine spaces Affine space Affine space is the set E with vector space \vec{E} and a transitive and free action of the additive \vec{E} on… Read More

Are you a complete beginner to the broad spectrum of Machine Learning? Are you torn between R, Python, GNU Octave, and all the other computer… Read More

In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions… Read More

Variational Autoencoders Variational autoencoder was proposed in 2013 by Knigma and Welling at Google and Qualcomm. A variational autoencoder (VAE) provides a probabilistic manner for… Read More

The basic neural network design i.e. an input layer, few dense layers, and an output layer does not work well for the image recognition system… Read More

Managing the Machine Learning Projects isn’t an easy piece of cake for every ML enthusiast or a student/developer working on them. Even Gartner has concluded… Read More

Due to its simplicity, easy operation, capacity to protect against local optima, and the problem of derivatives free, Metaheuristic was frequently employed throughout the previous… Read More