Machine Learning
Interview Questions
What is Gradient Descent?

What is gradient descent, and how is it used in machine learning?

Gradient descent is an optimization algorithm used in machine learning to minimize the error or cost function of a model. It works by iteratively adjusting the parameters of the model to reduce the difference between the predicted values and the actual values in the dataset. The cost function is a mathematical expression that measures the difference between the predicted values and the actual values. The goal of gradient descent is to find the values of the model parameters that minimize the cost function.

The formula for gradient descent is:

ΞΈj:=ΞΈjβˆ’Ξ±1mβˆ‘i=1m(hΞΈ(x(i))βˆ’y(i))xj(i)\theta_{j} := \theta_{j} - \alpha\frac{1}{m}\sum_{i=1}^{m}(h_{\theta}(x^{(i)})-y^{(i)})x_{j}^{(i)}

where ΞΈj\theta_{j} is the jthj^{th} parameter (weight or bias), Ξ±\alpha is the learning rate, mm is the number of training examples, hΞΈh_{\theta} is the hypothesis function, x(i)x^{(i)} is the ithi^{th} training example, y(i)y^{(i)} is the corresponding label, and xj(i)x_{j}^{(i)} is the value of the jthj^{th} feature for the ithi^{th} training example.

The basic idea behind gradient descent is to calculate the gradient of the cost function with respect to each parameter in the model. The gradient gives us the direction of steepest descent, which is the direction of the fastest decrease in the cost function. We then update the parameters in the opposite direction of the gradient to minimize the cost function.

There are different variants of gradient descent:

  • Batch gradient descent computes the gradient of the cost function with respect to the parameters using the entire dataset
  • Stochastic gradient descent uses only one randomly selected data point to compute the gradient of the cost function
  • Mini-batch gradient descent uses a small batch of data points to compute the gradient.

Gradient descent is widely used in machine learning, particularly in deep learning, where the cost function is often non-convex and high dimensional. By iteratively updating the model parameters using the gradient, the algorithm can find the optimal solution that minimizes the cost function.

One concrete example of using gradient descent is in training a neural network to recognize handwritten digits. The neural network has multiple layers and millions of parameters, and the cost function is non-convex. Gradient descent is used to adjust the weights and biases of the neural network to minimize the difference between the predicted and actual values of the handwritten digits. The algorithm iteratively updates the parameters using the gradients of the cost function until it reaches a minimum.