Gradient descent is at the core of most machine learning models. It’s the optimization technique that trains models and neural networks by fine-tuning parameters to improve performance. In this post, we’ll explore what gradient descent is, how it works, and the different types used in machine learning.
What is Gradient Descent?
In simple terms, the "Gradient" refers to the rate of change or the slope of a function, and "Descent" means moving downhill. Gradient descent is an algorithm used to minimize the cost function of a model. It does this by adjusting model parameters (or weights) iteratively, reducing the error between predicted and actual values. The result is a more accurate and efficient model.
How It Works?
Imagine you're trying to find the lowest point in a valley. Here’s how gradient descent mirrors that process:
Gradient: Think of the gradient as the steepness of the slope. The steeper the gradient, the bigger the step the model takes to minimize error.
Learning Rate: This determines the size of each step. A learning rate that is too high might cause you to overshoot the valley’s lowest point, while one that’s too low can lead to a very slow journey to the bottom.
Cost Function: The ultimate goal of gradient descent is to minimize the cost function, the measure of error between the model’s predictions and the actual outcomes.
Each iteration moves the model’s parameters a little closer to the optimum, ensuring that, over time, the model improves its predictions.
Types of Gradient Descent?
There are three main types of gradient descent, each with its unique approach to updating model parameters:
Batch Gradient Descent:
Computes the error for every single point in the training set.
The model parameters are updated only after all training examples have been processed.
Stochastic Gradient Descent (SGD):
Updates the model parameters for each training example individually.
Each example contributes to the training, leading to more frequent updates.
Mini-Batch Gradient Descent:
Combines the strengths of both batch and stochastic gradient descent.
The training dataset is split into small batches, and updates are performed on each batch.
I’m presenting an illustration of gradient descent, think of it like a red ball rolling down a valley, looking for the lowest point. The ball adjusts its path just like how gradient descent finetunes a model’s parameters to reach the minimum error.
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