From Linear Regression to Gradient Descent
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In previous parts we discussed about linear regression, gradient descent and how to calculate the optimal slope and intercept using Gradient Descent.
In this article, we'll build an intuition for backpropagation, understand why it is necessary, and explore how the chain rule and gradient descent work together to enable neural network learning.
What is Backpropagation?
Backpropagation is an algorithm used to train neural networks.
It is a way to adjust the weights and biases of a neural network to reduce the error between the predicted output and the actual output.
In simple terms, it is a way for the neural network to learn from its mistakes.
This learning process is powered by an algorithm called backpropagation, one of the most important concepts in machine learning. Backpropagation provides a systematic way for a neural network to determine which internal parameters contributed to an error and how those parameters should be updated to reduce future mistakes.
Imagine teaching a student to solve math problems.
After each attempt, you compare the student's answer with the correct one, identify where mistakes occurred, and provide feedback. Over time, the student adjusts their approach and improves. Backpropagation works in a similar way: the network makes a prediction, calculates the error, traces that error backward through the network, and updates its parameters accordingly.
At its core, backpropagation combines two fundamental mathematical ideas:
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The Chain Rule from calculus, which helps determine how changes in one part of the network affect the final error.
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Gradient Descent, an optimization technique that uses those calculations to update the network's parameters in the direction that reduces error.
By repeatedly answering these questions across thousands or millions of training examples, neural networks gradually learn patterns hidden within data and become increasingly accurate.
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