In this era, everything is influenced by Artificial Intelligence, Machine Learning, and deep learning. Nowadays it is common to use very big datasets and we need fast and efficient optimization algorithms to get the leverage of this big amount of data. Training Neural Network is generally much harder than the other optimization problems in deep …

Logistic regression is one of the most commonly used algorithms for machine learning and is a building block for neural networks. It is really important to understand the concepts and the derivations of logistic regression. In this post we will explore the fundamentals of logistic regression and also concepts like maximum likelihood function, cross-entropy is …

Previously, we have discussed briefly the simple linear regression. Here we will discuss multiple regression or multivariable regression and how to get the solution of the multivariable regression. At the end of the post, we will provide the python code from scratch for multivariable regression. Motivation A single variable linear regression model can learn to …

Regression is a very fundamental concept in statistics, machine learning and in Neural Network. Imagine plotting the correlation between rainfall frequency and agriculture production in high school. Increase in rainfall generally increases agriculture production. Fitting a line to those points enables us to predict the production rate under different rain conditions. It was actually a …

To build an accurate machine learning model we need to have a proper understanding of the error. In forming predictions of a model there are three sources of error: noise, bias, and variance. Having proper knowledge of error and bias-variance would help us building accurate models and avoiding mistakes of overfitting and underfitting. In this …