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Gradients for multi class classification with Softmax

Krishna Sankar8 hours ago5 hours ago022 mins

In a multi class classification problem, the output (also called the label or class) takes a finite set of discrete values . In this post, system model for a multi class classification with a linear layer followed by softmax layer is defined. The softmax function transforms the output of a linear layer into values lying…

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  • Machine Learning

Gradients for Binary Classification with Sigmoid

Krishna Sankar1 month ago3 weeks ago113 mins

In a classification problem, the output (also called the label or class) takes a small number of discrete values rather than continuous values. For a simple binary classification problem, where output takes only two discrete values : 0 or 1, the sigmoid function can be used to transform the output of a linear regression model…

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  • Machine Learning

Gradients for linear regression

Krishna Sankar2 months ago2 months ago112 mins

Understanding gradients is essential in machine learning, as they indicate the direction and rate of change in the loss function with respect to model parameters. This post covers the gradients for the vanilla Linear Regression case taking two loss functions Mean Square Error (MSE) and Mean Absolute Error (MAE) as examples. The gradients computed analytically…

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