Gradient Descent

Related lectures (27)

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Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Covers optimization without constraints using the gradient method to find the function's minimum.

Covers the design and optimization of neural network models in deep learning.

Explores practical aspects of Riemannian trust-region optimization and introduces the truncated conjugate gradient method.

Covers the Newton method and its convergence properties near the optimal point.

Covers Newton's Method for optimization and discusses the caveats of indefiniteness in optimization problems.

Explores adaptive gradient methods and their impact on optimization scenarios, including AdaGrad, ADAM, and RMSprop.

Covers the algorithm of gradient descent, aiming to minimize a function by iteratively moving in the direction of the steepest decrease.

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.