Hi Pythonistas!
In the last post we have learned about learning rate. Today we are addressing a big question
How does a neural network actually learn from its mistakes?
The answer is the real magic behind deep learning: Backpropagation.
The Forward Pass: When the Network Makes a Guess
Whenever we feed data into a neural network:
- The input travels through each layer
- Activations fire
- The network produces a prediction
This is called the forward pass. It is simply the network making a guess based on its current weights.
Of course, the initial guesses are usually wrong. And that is perfectly fine because mistakes are the fuel for learning.
The Backward Pass: When the Network Learns
Once we get the prediction, we calculate the loss, which tells us how far the network was from the correct answer.
Then the real learning begins. Backpropagation performs three key tasks:
- It checks how much each weight contributed to the error
- It calculates gradients using the chain rule
- It sends these gradients to the optimizer to update the weights
This entire process is called the backward pass.
You can think of the training cycle like this:
Forward pass: Guessing
Backward pass: Correcting
The Chain Rule: The Engine Behind Backpropagation
Imagine a row of dominos. When you push one piece, the effect travels through the entire row until the final piece falls.
A small change in one weight affects a neuron That neuron affects the next layer That eventually affects the predictions and the loss
Backpropagation traces this influence in reverse and computes how sensitive each weight is to the final error. That sensitivity is the gradient.
Why Backpropagation Matters
Efficient: Backprop reuses calculations so it can train very deep networks without wasting computation.
Scalable: It works even when the model has millions or billions of parameters.
Universal: Any differentiable network architecture can be trained using backprop. CNNs, RNNs, LSTMs, Transformers all rely on it.
The Real Insight
Backpropagation does not simply say "you were wrong" .It gives detailed feedback:
- This neuron contributed a lot to the error,
- This neuron contributed a little
- This neuron did not matter this time
Every weight receives its own customized correction. That is why deep learning models improve in such a precise and controlled manner.
What I Learned This Week
Forward pass means making a prediction
Backward pass means correcting that prediction
Backpropagation uses the chain rule to compute gradients
Gradients guide the optimizer to improve weights
This process is what makes neural networks trainable in the first place
What’s Coming Next
Next week we will learn about overfitting