NN#5 Neural Networks Decoded: Concepts Over Code 0 like February 12, 2025Share this postLast Updated on February 12, 2025 by Editorial TeamAuthor(s): RSD Studio.ai Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Source: Analytics VidhyaIn our ongoing quest to unlock the brains of AI, weve built a foundation of understanding, from the neuron-inspired perceptron to the power of activation functions in creating non-linear models. Weve 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 doesnt 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. Its 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?The Challenge of Blame Assignment: Where Did We Go Wrong?Imagine a complex machine with thousands, millions, or even billions of interconnected Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post