Backpropagation
Key algorithm for training neural networks. Efficiently calculates the gradient (correction direction) of the loss function with respect to each network weight.
Key Concepts
Chain Rule
The chain rule is a formula for computing the derivative of a composite function. It is the key mathematical concept that underlies backpropagation.
Gradient
The gradient is a vector that points in the direction of the steepest ascent of a function. In backpropagation, the gradient is used to update the weights of the network.
Loss Function
The loss function is a measure of how well the network is performing. The goal of backpropagation is to minimize the loss function.
Detailed Explanation
Backpropagation is an algorithm that is used to train artificial neural networks. It is a supervised learning algorithm that works by propagating the error from the output layer of the network back to the input layer.
The backpropagation algorithm is composed of two main steps:
- Forward Pass: In the forward pass, the input data is fed to the network and the output is computed.
- Backward Pass: In the backward pass, the error is propagated from the output layer back to the input layer. The weights of the connections between neurons are then updated so that the error is minimized.
Real-World Examples & Use Cases
Image Recognition
Backpropagation is used to train the neural networks that are used in image recognition systems.
Natural Language Processing
Backpropagation is used to train the neural networks that are used in natural language processing systems.
Machine Translation
Backpropagation is used to train the neural networks that are used in machine translation systems.