Скачать или смотреть The “Aha!” Moment | How Backpropagation Makes Neural Networks Learn

Welcome back to our Neural Networks series! In this episode, we uncover the learning engine behind every neural network, Backpropagation.
After a network makes a prediction and measures how wrong it was using the loss function, the next big question is how the network learns from that mistake. Backpropagation is the process that answers this question. It sends the error backward through the network, telling each weight and bias how it should change to improve future predictions.
In this video, you’ll learn:
What backpropagation is and why it’s essential for learning


How error moves backward through a neural network


How each weight and bias learns how to adjust


Why learning happens gradually, not all at once


How backpropagation works together with the loss function and learning rate
Everything is explained visually and intuitively, using real-world examples, making this video perfect for beginners and anyone building a strong foundation in deep learning.

Chapters:
00:00 – Introduction
00:44 – From Error to Learning
01:46 – What Backpropagation Really Means
03:10 – How Weights Get Updated
05:13 – Learning Rate: How Big Should the Step Be?
06:00 – How All Neurons Learn Together
07:15 – Wrap-Up

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