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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) and deep learning (DL) to minimise a cost/loss function (e.g. in a linear regression).
Sep 12, 2024 · Gradient Descent (GD) is a widely used optimization algorithm in machine learning and deep learning that minimises the cost function of a neural network model during training.
Oct 9, 2024 · Gradient descent is an iterative optimization algorithm for finding the local minimum of a function. To find the local minimum of a function using gradient descent, we must take steps proportional to the negative of the gradient (move away from the gradient) of the function at the current point.
Sep 23, 2024 · Gradient Descent in Machine Learning: A Deep Dive. Learn how gradient descent optimizes models for machine learning. Discover its applications in linear regression, logistic regression, neural networks, and the key types including batch, stochastic, and mini-batch gradient descent. Updated Sep 23, 2024 · 15 min read.
Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. It trains machine learning models by minimizing errors between predicted and actual results.
Jul 26, 2022 · The gradient descent algorithm is an optimization algorithm mostly used in machine learning and deep learning. Gradient descent adjusts parameters to minimize particular functions to local minima. In linear regression, it finds weight and biases, and deep learning backward propagation uses the method.
Feb 5, 2019 · So, what is the gradient descent? At a theoretical level, gradient descent is a first order iterative method for finding the minimum of a function, and thus is very well suited to use with...
Feb 21, 2024 · What is Gradient Descent? Working of Gradient Descent Algorithm. Random Initialization and Generate Prediction. Calculate Cost/error. Update Parameters. Conclusion. Frequently Asked Questions. Need of Gradient Descent Algorithm. Following up on the previous example let’s understand the intuition behind gradient descent.
Sep 13, 2024 · So, this was the introductory post on gradient descent, that has been the working horse for deep learning optimization since the seminal paper on backpropogation that showed you could train neural nets by computing gradients.
Apr 8, 2023 · Gradient descent is an iterative optimization method used to find the minimum of an objective function by updating values iteratively on each step. With each iteration, it takes small steps towards the desired direction until convergence, or a stop criterion is met.
