Cross-entropy is a loss function commonly used in machine learning, particularly in classification tasks. It measures the difference between two probability distributions: the true labels and the predicted probabilities.
Neural Network is a machine learning model inspired by the human brain, designed to recognize patterns and solve complex tasks. It consists of interconnected units called neurons organized into layers
Activation functions in machine learning define how a neuron in a neural network processes input data and decides whether to pass it to the next layer.
Probability Distribution describes how the probabilities are distributed over the possible outcomes of a random variable. It can be discrete, where outcomes are countable (e.g., rolling a die), or continuous, where outcomes can take any value within a range (e.g., heights of people).
Probability is a measure of the likelihood that a particular event will occur, quantified as a number between 0 and 1, where 0 indicates impossibility and 1 indicates certainty. It is used to model uncertainty and make predictions about random events in various fields, including machine learning.
The chain rule is a fundamental theorem in calculus that provides a method for computing the derivative of a composite function. It allows us to understand how a change in one variable propagates through a series of dependent variables
The Gradient Descent algorithm has numerous variants, each developed to address specific issues or improve performance under certain conditions
Linear regression is a fundamental machine learning algorithm used to predict a continuous target variable based on one or more input features. PyTorch is a popular deep learning library that can be used to implement linear regression models efficiently.
Gradient Descent is an optimization algorithm used to minimize the loss function in machine learning and deep learning models. It is an iterative process that helps find the optimal parameters (weights) for a given model by minimizing the loss function.
Linear regression is one of the simplest and most widely used algorithms for predictive modeling. It models the relationship between a dependent (target) variable and one or more independent (input) variables by fitting a linear equation to the observed data
SVD is a powerful matrix factorization technique widely used in various fields such as statistics, signal processing, and machine learning. It decomposes a matrix into three other matrices, revealing the intrinsic properties of the original matrix
