In this video, we'll dive into the concept of backpropagation using the chain rule, focusing on a simple neural network example. 🧠✨
🔍 What We've Covered So Far:
In Part 1, we discussed the forward pass, where we started with inputs, passed through a hidden layer, and reached an output neuron. This process gave us a predicted value which was 0.95. However, our actual target value was 1. 📊➡️🔄
🔙 Now, Let's Work Backwards:
Calculating the Loss: We begin by calculating the loss. This loss depends on our predicted value. 📉
Our prediction is the output of the sigmoid activation function applied to a previous aggregation. 🧩
The aggregation in turn depends on the weights from the previous layer. 🏋️♂️
✨ Updating Weights Using the Chain Rule:
To show how a specific weight gets updated, we'll use the chain rule and partial derivatives. This tells us how much the loss changes with respect to
a specific weight. 🧮
Then using the old weight (which was randomly initialized), η (eta) - the learning rate and the gradient dL/dw we find out the new weight. 🔄📈
🌟 Stay Tuned for More!
By the end of this video, you'll have a solid understanding of how backpropagation works and how it helps in training neural networks.
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