What is backpropagation, and why do LLMs use it?

AI is changing every part of our lives, and one of the most interesting concepts in machine learning is backpropagation. Its used extensively behind the scenes, and is a fascinating concept.?I wanted to take a moment to share why backpropagation is so important.? Neural networks, a class of machine learning model inspired by the human brain, are trained using a technique called backpropagation. Layers of basic computer units known as neurons are coupled to form neural networks.

To get a neural network to carry out a particular activity, such as translating languages or picture recognition, requires training. Researchers must modify the weights, which are sometimes referred to as the strength of the connections between the neurons, to achieve this.? An effective method to determine how much each weight needs to change during training is backpropagation. It functions by calculating the variance between the expected output of the network and the actual, accurate output. The method can then calculate how much each connection contributed to the error by propagating the error backwards from the output layer to each of the hidden levels. The weights are then changed to reduce the degree of error.

The neural network continuously increases its accuracy on the training data by iteratively conducting this process of forward propagation to generate predictions and backpropagation to compute mistakes. The network eventually learns to complete the required task after many rounds of this. Modern large language models, like GPT-3, are trained using backpropagation on enormous datasets to discover patterns and relationships in human language. Backpropagation enables neural networks like LLMs to effectively learn from data.