Backpropagation
Backpropagation is a supervised learning algorithm used to train artificial neural networks by minimizing the error in predictions. Introduced in its modern form in 1986 by David E. Rumelhart, Geoffrey Hinton, and Ronald J. Williams, backpropagation calculates the gradient of the loss function concerning the weights of the network. This is achieved by propagating the error backward from the output layer to the input layer, enabling the model to adjust its weights through methods like gradient descent. Backpropagation revolutionized neural network training by making it computationally feasible for deep architectures. https://en.wikipedia.org/wiki/Backpropagation
The backpropagation process consists of two main phases: forward propagation and backward propagation. During forward propagation, inputs are passed through the network to generate predictions. In backward propagation, the algorithm calculates the error gradient for each weight by applying the chain rule of calculus to compute partial derivatives layer by layer. This systematic approach allows the model to learn from its mistakes iteratively, refining its weights to reduce the prediction error. Optimizations like momentum and adaptive learning rates further enhance the efficiency of the backpropagation process. https://www.turing.ac.uk/research/research-projects/mathematics-backpropagation
Despite its widespread adoption, backpropagation has limitations, such as vanishing and exploding gradients in deep networks. These challenges have been addressed through innovations like ReLU activation functions and batch normalization, which stabilize the training process. Backpropagation remains a foundational algorithm in machine learning and continues to evolve with advancements in computing hardware, such as GPUs and specialized accelerators like TPUs. Its integration into frameworks like TensorFlow and PyTorch has further simplified its implementation in modern AI applications. https://www.tensorflow.org/ https://pytorch.org/