I'm trying to learn neural networks by watching this series of videos and implementing a simple neural network in Python.

Here's one of the things I'm wondering about: I'm training the neural network on sample data, and I've got 1,000 samples. The training consists of gradually changing the weights and biases to make the cost function result in a smaller cost.

My question: Should I be changing the weights/biases on every single sample before moving on to the next sample, or should I first calculate the desired changes for the entire lot of 1,000 samples, and only then start applying them to the network?