What Backpropagation Meaning, Applications & Example
A supervised learning algorithm for training neural networks.
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
- Stochastic Backpropagation: Updates weights after each individual data point, leading to faster but noisier convergence.
- Mini-Batch Backpropagation: Updates weights after a small batch of data, balancing between speed and accuracy.
- Batch Backpropagation: Updates weights after the entire dataset is processed, providing stable convergence but requiring more memory.
Applications of Backpropagation
- Neural Network Training: Essential for training deep learning models by reducing prediction error.
- Image Recognition : Optimizes weights in convolutional networks for accurate object detection .
- Natural Language Processing (NLP) : Enhances model performance for tasks like translation and sentiment analysis .
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.