Backpropagation In Details بالعربي

This lecture explains how to calculate the gradient of a loss function with respect to any weight in a model using Backpropagation. 🔄 You’ll learn the step-by-step process of performing backpropagation through a numerical example, with detailed explanations of each calculation.

We’ll cover how to identify the specific weight for gradient computation, calculate gradients using the chain rule and partial derivatives, and update weights based on the computed gradient values. 🧠 This session includes a numerical example to illustrate backpropagation in action, providing practical insights into how backpropagation improves neural network performance by efficiently updating weights.

Main Objectives:

Understand the backpropagation process for calculating gradients in neural networks.

Learn to identify specific weights for gradient computation and apply the chain rule with partial derivatives.

Explore how to update weights based on computed gradients to optimize model performance.

Gain hands-on experience with a numerical example demonstrating backpropagation.

Acquire practical skills for implementing backpropagation in your programs to improve model accuracy.

#Backpropagation #NeuralNetworks #GradientCalculation #MachineLearning #ModelOptimization