Demystifying Gradient Descent in Machine Learning: A Beginner’s Guide

Introduction

Machine learning is a field of artificial intelligence that requires the use of algorithms to enable computer systems to learn from and make predictions on data. One of the most commonly used algorithms in machine learning is gradient descent. It is a method used to optimize the parameters of a model in order to reduce prediction errors. Gradient descent can be difficult to comprehend for individuals who are new to machine learning. In this article, we will take a step-by-step approach to demystify gradient descent in machine learning.

What is Gradient Descent?

Gradient descent is an iterative optimization algorithm that enables the minimization of a function by finding the optimal solution or weights in a model. It works by starting at a random point in a multi-dimensional space, then calculates the direction of steepest descent to reach the minimum point.

It is important to note that gradient descent works only with differentiable functions that have a single global minimum. The gradient descent algorithm’s goal is to minimize the cost function by computing the gradient and moving in the opposite direction of the gradient.

Types of Gradient Descent

There are three types of gradient descent algorithm, namely batch, stochastic, and mini-batch gradient descent.

1. Batch Gradient Descent: It computes the partial derivative of the cost function, through all the training samples, and updates the parameters, subsequently, after each epoch.

2. Stochastic Gradient Descent: It, on the other hand, identifies the gradients using one input example at a time. The weights are updated after each training example.

3. Mini-Batch Gradient Descent: The mini-batch gradient descent involves working with small batches of data to compute the gradients instead of the entire training set. It is more computationally efficient than the batch gradient descent and less “noisy” than stochastic gradient descent.

How Gradient Descent Works

The gradient descent algorithm initiates with initial random parameter values (weights). The algorithm then iteratively evaluates the cost function to estimate the direction of the gradient and move the weights in that direction.

The steps involved in the gradient descent algorithm are as follows:

1. The algorithm calculates the error or cost function for each prediction made by the model.

2. The partial derivative of the cost function with respect to each parameter is calculated.

3. The gradient of the parameters is calculated based on the partial derivatives of the cost function.

4. The weights are adjusted according to the gradient direction multiplied by the learning rate.

5. Steps 1-4 are repeated until the cost function converges.

Examples:

To illustrate gradient descent’s concept, let’s consider an example of a linear regression problem. Let’s assume we are trying to predict the salary of an employee from their years of experience. In this case, our model’s cost function will be the mean squared error (MSE).

Our goal is to find the values of the parameters, i.e., slope (weights) and y-intercept (bias), that minimize our cost function and make accurate predictions.

During the training process, the gradient descent algorithm calculates the partial derivatives of the cost function with respect to each parameter and uses it to update the weights and bias in the direction of steepest descent.

After several iterations, the algorithm converges to the minimum value of the cost function, and our model achieves the desired level of accuracy.

Conclusion

Gradient descent is an effective optimization algorithm for machine learning and helps in avoiding the problem of overfitting. With a good understanding of the gradient descent algorithm and its three variations, one can easily implement them in their machine learning models. It is essential to try and tune different hyperparameters to obtain the best results possible, given their specific dataset and model complexity. We hope that this beginner’s guide has given you a good head start in demystifying gradient descent in machine learning.

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