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What is Backpropagation?

# What is backpropagation, and how is it used in machine learning?

Backpropagation is a widely used algorithm for training artificial neural networks. It is used to calculate the gradient of the loss function with respect to the weights of the network. This gradient is then used to update the weights of the network using an optimization algorithm such as gradient descent.

The backpropagation algorithm works by recursively applying the chain rule of calculus to calculate the gradients of the loss function with respect to the weights of the network. The process starts at the output layer of the network and works backwards towards the input layer.

Let's consider a simple feedforward neural network with one hidden layer. The output of the network can be written as:

$y = f(\mathbf{w_2} \cdot f(\mathbf{w_1} \cdot \mathbf{x} + \mathbf{b_1}) + \mathbf{b_2})$

where $\mathbf{x}$ is the input vector, $\mathbf{w_1}$ and $\mathbf{w_2}$ are the weight matrices for the first and second layers respectively, $\mathbf{b_1}$ and $\mathbf{b_2}$ are the bias vectors for the first and second layers respectively, and $f$ is the activation function.

The goal of backpropagation is to calculate the gradients of the loss function $L$ with respect to the weights and biases of the network. The loss function can be written as a function of the output of the network:

$L = L(y)$

The gradients of the loss function with respect to the output of the network can be calculated using the chain rule:

$\frac{\partial L}{\partial y} = \frac{\partial L}{\partial y} \frac{\partial y}{\partial \mathbf{z_2}}$

where $\mathbf{z_2} = \mathbf{w_2} \cdot f(\mathbf{w_1} \cdot \mathbf{x} + \mathbf{b_1}) + \mathbf{b_2}$ is the input to the output layer.

The gradients of the loss function with respect to the weights and biases of the output layer can then be calculated using the gradients of the loss function with respect to the output of the network:

$\frac{\partial L}{\partial \mathbf{w_2}} = \frac{\partial L}{\partial y} \frac{\partial y}{\partial \mathbf{z_2}} \frac{\partial \mathbf{z_2}}{\partial \mathbf{w_2}}$
$\frac{\partial L}{\partial \mathbf{b_2}} = \frac{\partial L}{\partial y} \frac{\partial y}{\partial \mathbf{z_2}} \frac{\partial \mathbf{z_2}}{\partial \mathbf{b_2}}$

The gradients of the loss function with respect to the input to the output layer can be calculated using the gradients of the loss function with respect to the output of the network and the weights of the output layer:

$\frac{\partial L}{\partial \mathbf{z_2}} = \frac{\partial L}{\partial y} \frac{\partial y}{\partial \mathbf{z_2}}$

Using the chain rule of differentiation, we can calculate the gradients of the loss function with respect to the weights and biases of the hidden layer as:

$\frac{\partial L}{\partial w_h} = \frac{\partial L}{\partial y}\cdot\frac{\partial y}{\partial h}\cdot\frac{\partial h}{\partial w_h}$
$\frac{\partial L}{\partial b_h} = \frac{\partial L}{\partial y}\cdot\frac{\partial y}{\partial h}\cdot\frac{\partial h}{\partial b_h}$

Here, $w_h$ and $b_h$ are the weights and biases of the hidden layer, respectively. $L$ is the loss function, $y$ is the output of the neural network, and $h$ is the input to the hidden layer.

The first term in each equation is the gradient of the loss function with respect to the output $y$. The second term is the gradient of the output with respect to the input to the hidden layer $h$. The third term is the gradient of the input to the hidden layer with respect to the weights or biases.

Once we have calculated the gradients of the loss function with respect to the weights and biases of the hidden layer, we can update them using an optimization algorithm such as gradient descent to improve the performance of the neural network.