Introduction to Backpropagation in Machine Learning
Machine learning is all about teaching models to learn new things through backpropagation. We start with a series of matrices that need to be trained. First, we input a question and see how the model responds.
If the answer is off, we measure the difference, or delta. Calculating the delta is done through backpropagation. At the start, the model's answer is typically various degrees of wrong, almost random.
Our task is to gauge how off the mark it is from the actual answer. Suppose we feed it the word "hello" and want the model to determine if it's positive or negative in tone. We'd pass "hello" through a tokenizer to turn it into an integer.
This integer goes into an embedding, becoming a sequence of floating-point numbers composing our first matrix, the input matrix. We then proceed with the feedforward operation. It starts with the input layer, performs matrix multiplication with the relevant layer, and generates an output.
We continue this process with every layer of the model until we get our final output target. This entire sequence takes the form of a visual diagram, with the input layer at the start, matrix multiplication happening with each layer, culminating in the output layer. At this stage, the model forms an opinion, say, that "hello" is a positive word.
But it could be more positive than the model gives it credit for. Therefore, we need to gauge how off the mark it is. To do this, we go back through the output and calculate the delta.
The difference between what we think and what the model thinks is the basis for further learning. Next, we invert the arrangement and go backward through the matrix, calculating and comparing the output with the input. Corrections are determined layer by layer, maximizing the accuracy of the model.
This process is repeated, with each layer slightly adjusted according to the delta. Balancing this learning is crucial, as leaning too heavily toward one data set leads to others being forgotten. By gradually incorporating different inputs, the model broadens its knowledge base.
This precision-oriented adjustment of matrices relies on the learning rate, which ensures we don't overcorrect and create new errors in the process. Training a model is all about working iteratively with backpropagation to track and correct small inaccuracies.