2024年11月2日 · Backpropagation is a powerful algorithm in deep learning, primarily used to train artificial neural networks, particularly feed-forward networks. It works iteratively, minimizing the cost function by adjusting weights and biases.

In machine learning, backpropagation[1] is a gradient estimation method commonly used for training a neural network to compute its parameter updates. It is an efficient application of the chain rule to neural networks.

2019年8月8日 · The algorithm is used to effectively train a neural network through a method called chain rule. In simple terms, after each forward pass through a network, backpropagation performs a backward pass while adjusting the model’s parameters (weights and biases).

2024年10月23日 · Backpropagation is the neural network training process of feeding error rates back through a neural network to make it more accurate. Here’s what you need to know.

2015年3月17日 · Backpropagation is a common method for training a neural network. There is no shortage of papers online that attempt to explain how backpropagation works, but few that include an example with actual numbers.

2024年6月12日 · Backpropagation is the essence of neural network training. It is the method of fine-tuning the weights of a neural network based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and make the model reliable by increasing its generalization.

Backpropagation is a machine learning technique essential to the optimization of artificial neural networks. It facilitates the use of gradient descent algorithms to update network weights, which is how the deep learning models driving modern artificial intelligence (AI) “learn.”