Gradient Descent is an iterative optimization algorithm used to find the minimum (or maximum) of a function, typically used in the context of training machine learning models. Its primary objective is to adjust the parameters of a model in the direction that reduces the value of a given loss function, allowing the model to better fit the training data and make more accurate predictions.
Here's how the gradient descent algorithm works…
Initialization: The algorithm starts with an initial guess for the model's parameters (weights and biases). These parameters represent the internal variables that the model uses to make predictions.
Forward Pass: The input data is fed into the model, and the forward propagation process computes the model's predictions for the given inputs using the current values of the parameters.
Loss Computation: The loss function is evaluated, measuring how well the model's predictions match the true target values.
Backpropagation: The key step in gradient descent is to compute the gradients of the loss function with respect to each model parameter.
Gradient Update: With the gradients calculated, the algorithm updates the model's parameters by subtracting a small fraction (learning rate) of the gradients from their current values.
Iteration: Repeat the above, each time adjusting the parameters in the direction that reduces the loss. The algorithm continues until a stopping condition is met, such as a maximum number of iterations or when the change in loss becomes negligible.
Variants of gradient descent, such as stochastic gradient descent (SGD), mini-batch gradient descent, and adaptive learning rate methods (e.g., Adam), introduce additional optimizations and efficiencies to improve the convergence speed and stability of the algorithm during model training.