Neural networks are powerful machine learning models that can be trained to perform complex tasks. At the heart of training a neural network is a concept called backpropagation — a method for calculating gradients needed for neural network optimization.
Understanding Terms:
Weights and Biases
Neural network weights and biases represent the parameters that are optimized during training. Weights measure the importance of connections between neurons, while biases shift the activation function to the left or right.
Activation Functions
Neurons in the network apply activation functions to their input to determine their output. Common functions include sigmoid, ReLU and tanh. These nonlinearities enable the network to learn complex patterns from data.
Loss Function
The loss function measures how far the network predictions are from the actual target values. During training, we strive to minimize this loss by adjusting the network weights and biases.
Gradient Descent
Using gradient descent, the network weights and biases are adjusted in the direction that minimizes the loss function. The gradients — derivatives of the loss with respect to the parameters — identify this direction.
Backpropagation derives these gradients, allowing weights and biases to be optimized through iterative gradient descent. The name derives from information propagating “backwards” from later layers.
Backpropagation enables neural networks to be trained effectively. Without it, we would be unable to tune a network’s millions of parameters to perform complex tasks with stunning accuracy.
How Backpropagation Really Works:
Backpropagation is an ingenious algorithm that makes deep learning possible. But how exactly does it work its magic? Let’s demystify backpropagation in simple terms.
Imagine a neural network with multiple layers of neurons. During the forward pass, an input is fed forward and calculations are done to produce an output. Now comes the tricky part — training the network by updating its weights.
This is where backpropagation comes in. It works in two phases:
1.The Forward Pass
An input is fed forward through the network, layer by layer. At each neuron, an activation function is applied to calculate the output. The network’s final output is compared to the desired output to calculate the error.
2.The Backward Pass
Here’s the magic — we propagate this error backwards through the network! Starting from the final layer, the error gradient is calculated for each weight — showing by how much that weight contributed to the error.
These gradients — or partial derivatives — are then used to calculate new weights that will minimize the error on the next pass. The process is repeated for every weight and bias in the network, from the final layer backwards to the first hidden layer.
By calculating these gradients, backpropagation effectively “tells” the network how to minimize its loss function — enabling powerful optimization algorithms like gradient descent to do their work.
After several forward and backward passes through many training examples, the network weights slowly adjust to minimize total error — resulting in a trained network that can generalize to new inputs.
