The Mathematics of Backpropagation: A Step-by-Step Guide

Описание: In this comprehensive lecture, we dive into the core mathematical principles of backpropagation, also known as the backward propagation of errors. Backpropagation is a fundamental algorithm used to train neural networks. Its main goal is to reduce the difference between a model's predicted output and its actual output by iteratively adjusting weights and biases.
We'll break down key concepts, including: The role of the chain rule from calculus. How gradient descent is used to minimize the cost function.
The concept of an epoch as a full forward and backward pass through the training data.
An introduction to different optimizers, like the Adam Optimizer, which is used to update weights and biases. We will walk through a simple network example to illustrate the forward and backward passes. Grab your notebooks and follow along with the calculations to master the math behind backpropagation.

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