Backpropagation

2024 | AI Dictionary

What is Backpropagation: An optimization algorithm that trains neural networks by calculating gradients to update weights and minimize errors.

What is Backpropagation?

Backpropagation is an optimization algorithm used in training neural networks. It calculates the gradient of the loss function with respect to each weight in the network, allowing the model to update weights to minimize error and improve accuracy.

Types of Backpropagation

  1. Stochastic Backpropagation: Updates weights after each individual data point, leading to faster but noisier convergence.
  2. Mini-Batch Backpropagation: Updates weights after a small batch of data, balancing between speed and accuracy.
  3. Batch Backpropagation: Updates weights after the entire dataset is processed, providing stable convergence but requiring more memory.

Applications of Backpropagation

Example of Backpropagation

An example of Backpropagation is in training a Convolutional Neural Network (CNN) for image classification , where it adjusts weights to improve the network’s accuracy in recognizing objects.

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