Gradient Descent
Optimization algorithm that updates neural network weights by taking small steps in the opposite direction of the gradient to minimize error.
Key Concepts
Perception
The ability to interpret and understand sensory data from the environment, including vision, hearing, and other forms of input processing.
Reasoning
The capacity to process information logically, make inferences, and solve complex problems based on available data and learned patterns.
Action
The ability to execute decisions and interact with the environment to achieve specific goals and objectives effectively.
Learning
The capability to improve performance and adapt behavior based on experience, feedback, and new information over time.
Detailed Explanation
Gradient Descent is an iterative optimization algorithm used to find the minimum of a function. In the context of machine learning, its goal is to minimize the "cost function" (or "loss function"), which measures the error between a model's predictions and the actual values. By reducing this error, the model becomes more accurate.
How Gradient Descent Works
A common analogy to understand it is to imagine that you are on a mountain with your eyes blindfolded and your goal is to reach the valley (the lowest point). You can only feel the slope of the terrain under your feet. A logical strategy would be to take steps in the direction where the slope is steepest downwards. This process is repeated until you reach a flat point, which would be the bottom of the valley.
- The mountain represents the cost function.
- Your current position is the model's parameters (weights and biases).
- The direction of the steepest slope is the gradient of the cost function.
- The size of your steps is known as the "learning rate."
Real-World Examples & Use Cases
Online Advertising
Platforms like Google Ads and Facebook Ads use it to optimize ad performance, predicting the probability that a user will click on an ad.
Neural Networks and Deep Learning
It is the most widely used algorithm for training neural networks, adjusting the parameters to minimize the difference between the actual output and the one estimated by the network.
Linear Regression
It is used to find the line of best fit in a dataset, minimizing the mean squared error between the predictions and the actual data.