twitter graph

twitter graph

Comments

Post a Comment

Popular posts from this blog

variants

Image
  Gradient Descent algorithm and its variants Difficulty Level :   Easy Last Updated :   15 Feb, 2023 Read Discuss Courses Practice Video Gradient descent is a powerful optimization algorithm used to minimize the loss function in a machine learning model. It’s a popular choice for a variety of algorithms, including linear regression, logistic regression, and neural networks. In this article, we’ll cover what gradient descent is, how it works, and several variants of the algorithm that are designed to address different challenges and provide optimizations for different use cases. What is Gradient Descent? Gradient descent is an optimization algorithm that is used to minimize the loss function in a machine learning model. The goal of gradient descent is to find the set of weights (or coefficients) that minimize the loss function. The algorithm works by iteratively adjusting the weights in the direction of the steepest decrease in the loss function. How does Gradient Descent...
Read more

General Statistical Models

  General Statistical Models The time has come to learn some theory. This is a preview of STAT 5101–5102. We don’t need to learn much theory. We will proceed with the general strategy of all introductory statistics: don’t derive anything, just tell you stuff. 3.1  Probability Models 3.1.1  Kinds of Probability Theory There are two kinds of probability theory. There is the kind you will learn in STAT 5101-5102 (or 4101–4102, which is more or less the same except for leaving out the multivariable stuff). The material covered goes back hundreds of years, some of it discovered in the early 1600’s. And there is the kind you would learn in MATH 8651–8652 if you could take it, which no undergraduate does (it is a very hard Ph. D. level math course). The material covered goes back to 1933. It is all “new math”. These two kinds of probability can be called  classical  and  measure-theoretic , respectively. The old kind (classical) is still very useful. For every 100...
Read more

Difference between Batch Gradient Descent and Stochastic Gradient Descent

Image
  Difference between Batch Gradient Descent and Stochastic Gradient Descent In order to train a Linear Regression model, we have to learn some model parameters such as feature weights and bias terms. An approach to do the same is Gradient Descent which is an iterative optimization algorithm capable of tweaking the model parameters by minimizing the cost function over the train data. It is a complete algorithm i.e it is guaranteed to find the global minimum (optimal solution) given there is enough time and the learning rate is not very high. Two Important variants of Gradient Descent which are widely used in Linear Regression as well as Neural networks are Batch Gradient Descent and Stochastic Gradient Descent(SGD).  Batch Gradient Descent:  Batch Gradient Descent involves calculations over the full training set at each step as a result of which it is very slow on very large training data. Thus, it becomes very computationally expensive to do Batch GD. However, this is gre...
Read more