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Gradient Descent is an algorithm that finds the best-fit line for linear regression for a training dataset in a smaller number of iterations.
when it comes to building the Deep Learning models, the Gradient Descent has some major challenges. Let's explore them in this article
Vector Norms are non-negative values. In this article, find the different ways to calculate Vector Norms in machine learning and data science
This article is focused on the types of gradient descent algorithm that is essential for understanding Deep learning in detail. Start Reading Now!
Logistic Regression is a mathematical model used in statistics to estimate the probability of an event occurring using some previous data
Gradient descent is a first-order iterative optimization algorithm. In this article, learn how does gradient descent work and optimize model
Cost function gives the lowest MSE which is the sum of the squared differences between the prediction and true value for Linear Regression
Learn how Binary Cross Entropy (Log Loss) functions in binary classification tasks in machine learning. Understand its formula, application, and role in optimizing classification models
Explore the components of a neural network and learn about neural network layers and neurons, including input, hidden, and output layers.
In this article understand the basics of Decision Trees such as Decision Tree Split, ideal split, pure nodes with a video
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