Author(s): RSD Studio.ai

Originally published on Towards AI.

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In our ongoing quest to unlock the brains of AI, we’ve built a foundation of understanding, from the neuron-inspired perceptron to the power of activation functions in creating non-linear models. We’ve even equipped our models with a “compass” in the form of loss functions, allowing them to measure the discrepancy between their predictions and the real world. But possessing that compass doesn’t inherently ensure correct navigation. The next question is, How can our models know when the needle has gone astray? and how to correct them?

This is where backpropagation enters the story.

Backpropagation is the ingenious algorithm that allows neural networks to truly learn from their mistakes. It’s the mechanism by which they analyze their errors and adjust their internal parameters (weights and biases) to improve their future performance. Just as a skilled musician tunes their instrument to produce harmonious sounds, backpropagation allows neural networks to “tune” themselves, gradually refining their predictions until they resonate with the underlying patterns in the data. So, how do machine brains “tune”?

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Published via Towards AI