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Linear Regression
Manage episode 443721461 series 3605861
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This episode is about linear regression, a fundamental statistical method used to predict a numerical value based on a set of features (input variables). It describes the key components of linear regression, including the model (a linear function that relates features to the target), the loss function (which quantifies the error between predictions and actual values), and the optimization algorithm (minibatch stochastic gradient descent) used to find the best model parameters. The text also highlights the connection between linear regression and the normal distribution, demonstrating how minimizing the squared loss is equivalent to maximizing the likelihood of the data under the assumption of additive Gaussian noise. Finally, it explains how linear regression can be viewed as a single-layer neural network, illustrating the relationship between traditional statistical methods and the more complex models used in deep learning.
For more, check out: https://d2l.ai/chapter_linear-regression/linear-regression.html
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