Gradient Descent
What is Gradient Descent?
The "gradient" is a term used in mathematics and computer science to describe the rate of change of a function. It's a fancy way of saying how much something is changing over time, or in other words, how steep a line is.
Gradient descent is a machine-learning technique that reduces a model's error by changing its parameters. It's an iterative process that works by calculating the gradient of the loss function concerning each parameter in the model and then adjusting the parameters in the direction of the steepest descent.
Why is Gradient Descent Important in Machine Learning?
Gradient descent is essential in machine learning because it allows us to optimize a model's parameters and improve its performance. By minimizing the model's error, we can make better predictions and get more accurate results.
How Does Gradient Descent Work in Machine Learning?
Gradient Descent constitutes primarily 4 steps: -
Random Initialization
Generating Predictions
Calculating Cost
Updating Parameters
Let's understand this one by one!
Random Initialization
The initial step in the Gradient Descent process involves assigning arbitrary values to the model's parameters, which include the weights and biases utilized for making predictions. By doing so, the model is endowed with a vast range of options, allowing it to explore various paths toward determining the most favorable solution.
Generating Predictions
After initializing the model parameters, the next step involves generating predictions by feeding the input data into the model and applying the weights and biases. For instance, in the case of predicting the price of a house based on its size, the model takes the house size as input and produces an output that represents the predicted price.
Calculating Cost
The third stage involves computing the cost or loss function, which assesses the degree of concurrence between the model's predictions and the actual output. It quantifies the variance between the predicted and actual output by determining the difference between them. The primary objective is to minimize the cost function by reducing the discrepancy between the predicted and actual output.
Updating Parameters
In the last step, the model parameters are updated using the cost function, aiming to minimize the cost and enhance the accuracy of predictions. This process is repeated iteratively until the model achieves a satisfactory level of precision. Updating the parameters based on the cost function enables the model to learn from its errors and refine its predictions through adjustments.
That's the end of the article readers!
Will be explaining more in my following blogs!
Creating a machine learning model is like exploring a new world. You start with random values and predictions, but with each step, you learn from the results and adjust your path. Persistence and accuracy eventually lead to success." - Andrew Ng
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