Gradient descent algorithm

 Think about how a machine learns from the data in machine learning and deep learning during training. This involves a large amount of data.

Gradient Descent in Machine Learning

What is Gradient?

A gradient is nothing but a derivative that defines the effects on outputs of the function with a little bit of variation in inputs.


What is Gradient Descent?

Gradient Descent stands as a cornerstone orchestrating the intricate dance of model optimization. At its core, it is a numerical optimization algorithm that aims to find the optimal parameters—weights and biases—of a neural network by minimizing a defined cost function.


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. It works by iteratively adjusting the weights or parameters of the model in the direction of the negative gradient of the cost function until the minimum of the cost function is reached.


The learning happens during the backpropagation while training the neural network-based model. There is a term known as Gradient Descent, which is used to optimize the weight and biases based on the cost function. The cost function evaluates the difference between the actual and predicted outputs.


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.

The algorithm calculates gradients, representing the partial derivatives of the cost function concerning each parameter.

These gradients guide the updates, ensuring convergence towards the optimal parameter values that yield the lowest possible cost.


Gradient Descent is versatile and applicable to various machine learning models, including linear regression and neural networks. Its efficiency lies in navigating the parameter space efficiently, enabling models to learn patterns and make accurate predictions. Adjusting the learning rate is crucial to balance convergence speed and avoiding overshooting the optimal solution.



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