Backpropagation through structure
Backpropagation is a process used by computers to learn and improve their performance on a particular task. It follows a structure where information is passed forward and backward through a neural network. The neural network is a set of connected nodes that are responsible for processing the information.
Think of it like a toy train set with blocks and rails. Each block is a node and one rail connects to another rail like a node. The train set is fed some information which goes through it and comes back out with an output. Suppose the train set needs to know how to differentiate between different coloured blocks. The input (block colour) and output (correct identification) are passed through the nodes and they adjust themselves to learn which block is which colour.
Backpropagation is the process of adjusting the weights on the connections between the nodes to improve the accuracy of the output. With each pass forward and backward, the weights between the nodes are adjusted to improve the accuracy of the network's output.
With each iteration, the computer learns more about colours and their associations to the coloured blocks in order to become better at correctly identifying the colours of blocks. As the computer learns, it can transfer that knowledge to new sets of data and perform better in recognizing colours of new blocks.
So the process of backpropagation through structure is essentially fine-tuning the neural network's connections to make it more accurate in processing data. Like a toy that gets better and better at identifying block colours as it chugs along, the neural network adjusts its weights to continually improve in its accuracy.
Think of it like a toy train set with blocks and rails. Each block is a node and one rail connects to another rail like a node. The train set is fed some information which goes through it and comes back out with an output. Suppose the train set needs to know how to differentiate between different coloured blocks. The input (block colour) and output (correct identification) are passed through the nodes and they adjust themselves to learn which block is which colour.
Backpropagation is the process of adjusting the weights on the connections between the nodes to improve the accuracy of the output. With each pass forward and backward, the weights between the nodes are adjusted to improve the accuracy of the network's output.
With each iteration, the computer learns more about colours and their associations to the coloured blocks in order to become better at correctly identifying the colours of blocks. As the computer learns, it can transfer that knowledge to new sets of data and perform better in recognizing colours of new blocks.
So the process of backpropagation through structure is essentially fine-tuning the neural network's connections to make it more accurate in processing data. Like a toy that gets better and better at identifying block colours as it chugs along, the neural network adjusts its weights to continually improve in its accuracy.