Decoding Neural Networks: Forward vs. Backward Propagation

Neural networks are a foundational element of deep learning, and the primary operations performed during the training of a neural network are forward propagation and backward propagation. Let’s delve into both:

Forward Propagation:

Forward propagation refers to the initial phase where information is passed forward through the network. Given an input, it computes an output through all the layers of the neural network.

  1. Process:
    • Begin with an input data point (or a batch of data points).
    • Pass this input through each layer of the neural network by multiplying with the layer’s weights and adding the biases.
    • Apply an activation function (like ReLU, sigmoid, or tanh) to the result.
    • Repeat until the data reaches the output layer.
  2. Purpose:
    • To compute the predicted output (a hypothesis) for the given input.
    • This output is then compared with the true output to compute the error or loss.

Backward Propagation (or Backpropagation):

Once the forward pass is completed, the neural network uses the computed error to update its weights and biases. This process of computing the gradients and updating the weights is called backward propagation.

  1. Process:
    • Calculate the loss by comparing the predicted output from forward propagation to the actual target values.
    • Using calculus, compute the gradient of this loss concerning each weight in the network (essentially, how much does the loss change if a weight is changed by a tiny amount). This is done using the chain rule.
    • Starting from the output layer and working backward to the input layer, propagate these gradients through the network. For each weight, you’ll determine how it contributed to the error.
    • Update the weights and biases of the network using the computed gradients. This step often uses optimization algorithms like Gradient Descent or its variants (Adam, RMSprop, etc.).
  2. Purpose:
    • To minimize the error in the network’s predictions.
    • By adjusting the weights and biases using the gradients, the network learns to make better predictions.

A Simple Analogy:

Imagine you’re trying to tune a radio to your favorite station.

  • Forward Propagation: This is akin to hearing the sound from the radio when you’re on a particular frequency. If it’s static, you know you’re not on the right station. If it’s clear music or voice, you’re close or right on the station.
  • Backward Propagation: Based on the sound (static or clear signal), you decide whether to move the tuning dial left or right, and by how much. This is like adjusting the weights based on the error: if the prediction is way off (a lot of static), you adjust more; if it’s close (minimal static), you adjust just a bit.

In essence, neural networks “learn” using this repeated process of forward and backward propagation, iteratively adjusting weights to minimize error across the training data.