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  1. www.javatpoint.com › gradient-descent-in-machineGradient Descent in Machine Learning - Javatpoint

    In this tutorial on Gradient Descent in Machine Learning, we will learn in detail about gradient descent, the role of cost functions specifically as a barometer within Machine Learning, types of gradient descents, learning rates, etc.

  2. www.javatpoint.com › gradient-descent-algorithmGradient Descent Algorithm - Javatpoint

    The Gradient Descent is an optimization algorithm which is used to minimize the cost function for many machine learning algorithms. Gradient Descent algorithm is used for updating the parameters of the learning models. Following are the different types of Gradient Descent:

  3. www.javatpoint.com › how-to-implement-gradientHow to Implement Gradient Descent Optimization ... - Javatpoint

    Understanding how gradient descent works, being able to use it from the basics and technical knowledge is important for any fact scientist or gadget aspirant in this tutorial we go into details of gradient descent step-through-step manual for implementing Python half for from scratch.

  4. www.geeksforgeeks.org › gradient-descent-algorithmGradient Descent Algorithm in Machine Learning

    Sep 12, 2024 · Gradient Descent is a fundamental optimization algorithm in machine learning used to minimize the cost or loss function during model training. It iteratively adjusts model parameters by moving in the direction of the steepest decrease in the cost function.

  5. www.geeksforgeeks.org › ml-stochastic-gradientML | Stochastic Gradient Descent (SGD) - GeeksforGeeks

    Mar 14, 2024 · Stochastic Gradient Descent (SGD) is a variant of the Gradient Descent algorithm that is used for optimizing machine learning models. It addresses the computational inefficiency of traditional Gradient Descent methods when dealing with large datasets in machine learning projects.

  6. www.tutorialspoint.com › what-are-the-differentWhat are the different kinds of gradient descent algorithms ...

    Dec 11, 2020 · The idea behind using gradient descent is to minimize the loss when in various machine learning algorithms. Mathematically speaking, the local minimum of a function is obtained. To implement this, a set of parameters are defined, and they need to be minimized. Once the parameters are assigned coefficients, the error or loss is calculated.

  7. www.geeksforgeeks.org › ml-momentum-based-gradientMomentum-based Gradient Optimizer introduction - GeeksforGeeks

    Mar 1, 2023 · The Momentum-based Gradient Optimizer has several advantages over the basic Gradient Descent algorithm, including faster convergence, improved stability, and the ability to overcome local minima. It is widely used in deep learning applications and is an important optimization technique for training deep neural networks.