AI Tutorial

Gradient Descent in Machine Learning: Algorithm, Types, Optimization

Introduction
What is Gradient Descent in Machine Learning?
Why Is It Called Gradient Descent?
Gradient Descent Algorithm
Types of Gradient Descent
How are The Parameters Updated During Gradient Descent Process?

FAQs About Gradient Descent in Machine Learning

How does Gradient Descent work?

Gradient Descent works by calculating the gradient (direction and rate of the steepest increase) of the loss function and then updating the model's parameters in the opposite direction of the gradient, thereby reducing the loss.

Why is the Learning Rate important in Gradient Descent?

The learning rate determines the size of the steps taken towards the minimum. If it's too large, the algorithm might overshoot the minimum; if it's too small, it may take too long to converge or get stuck in a local minimum.

What is the difference between Gradient Descent and Stochastic Gradient Descent?

The key difference lies in the data used for each update. Gradient Descent uses the entire dataset to compute the gradient and update parameters, making it computationally intensive. Stochastic Gradient Descent updates parameters using the gradient calculated from a single data point, making it faster but potentially more erratic.

How do you choose the Learning Rate?

The learning rate is typically chosen through experimentation. It may also be dynamically adjusted during training (learning rate scheduling) or set using algorithms like AdaGrad, RMSProp, or Adam, which adaptively change the learning rate.

What are common issues with Gradient Descent?

Common issues include choosing an inappropriate learning rate, getting stuck in local minima (especially in non-convex problems), and slow convergence with large datasets.

How is Gradient Descent used in Neural Networks?

In neural networks, Gradient Descent is used to update the weights and biases of the network by calculating the gradient of the loss function with respect to each weight and bias, then adjusting them to minimize the loss.

Can Gradient Descent be used for all types of Machine Learning models?

While Gradient Descent is versatile, its effectiveness depends on the model and the nature of the loss function. It is most commonly used in models where the parameters are continuous and differentiable.

What are the advancements in Gradient Descent?

Advancements include the development of adaptive learning rate methods (like Adam and RMSProp), the use of momentum to accelerate convergence in relevant directions and dampen oscillations, and techniques to avoid local minima.