Backpropagation explained (Maths Behind AI)

What is Backpropagation and how do neural networks actually learn? In this video, we visually explain **backpropagation**, the core algorithm that enables neural networks to learn from data in Machine Learning and Deep Learning. Backpropagation works by calculating how errors in the output of a neural network can be traced back through each layer to adjust parameters like weights and biases. Using concepts like gradients and the chain rule, the model learns how to reduce its error and improve predictions over time. Through clear visual animations, this video demonstrates how errors flow backward through the network and how each layer updates its parameters during training. In this video you will learn: • What backpropagation is • How errors are calculated using a cost function • How gradients are computed using the chain rule • How weights and biases are updated • How backpropagation works with gradient descent Backpropagation is the key mechanism behind how modern AI systems learn, including image recognition, language models, and many real-world applications. This channel explains Artificial Intelligence concepts using clear visual explanations to make complex ideas simple and intuitive. Subscribe for more videos on: Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, and the mathematics behind AI. #artificialintelligence #machinelearning #deeplearning #backpropagation #aiexplained #agenticai #generativeai #maths