Jan 23, 2025 · Gradient descent minimizes the Mean Squared Error (MSE) which serves as the loss function to find the best-fit line. Gradient Descent is used to iteratively update the weights (coefficients) and bias by computing the gradient of the MSE with respect to these parameters.

Jan 23, 2025 · Gradient Descent is a fundamental algorithm in machine learning and optimization. It is used for tasks like training neural networks, fitting regression lines, and minimizing cost functions in models.

Nov 14, 2020 · Gradient descent is an Optimization algorithm that is used in deep learning to minimize the cost function w.r.t. the model parameters. It does not guarantee convergence to the global minimum. The convergence depends on the start point, learning rate and number of …

Feb 17, 2024 · Gradient descent (GD) is a mechanism in supervised learning to learn parameters of neural network by navigating the error surface in an efficient and pricipled way. It is used to find the...

Apr 10, 2025 · We perform descent along the direction of the gradient. Hence, it’s called Gradient Descent. Once we have the direction we want to move in, we must decide the size of the step we must take. The size of this step is called the learning rate. We must choose it carefully to ensure we can get to the minimum.

Feb 20, 2025 · Gradient descent is central to modern machine learning and optimization. Whether you’re training a neural network, building a regression model, or fine-tuning a recommendation system, chances are you’ll rely on gradient descent to make everything run smoothly.

May 22, 2021 · Gradient descent (GD) is an iterative first-order optimisation algorithm, used to find a local minimum/maximum of a given function. This method is commonly used in machine learning (ML)...

In this paper, we aim at providing an introduction to the gradient descent based op-timization algorithms for learning deep neural network models. Deep learning models in-volving multiple nonlinear projection layers are very challenging to train.

Data scientists implement a gradient descent algorithm in machine learning to minimize a cost function. The algorithm is typically run first with training data, and errors on the predictions are used to update the parameters of a model. This helps to reduce errors in …

Oct 25, 2024 · Differentiate between Batch Gradient Descent, Stochastic Gradient Descent (SGD), and Mini-Batch Gradient Descent, covering their mechanics and how they update model parameters. Describe how gradients indicate a direction for optimization and the importance of setting an appropriate learning rate to ensure effective and efficient convergence.