Hey there, fellow learner! I totally get it – back propagation can be a daunting concept to grasp, especially when you’re self-taught and not from a traditional computer science background. I’m here to help you break it down in a simple way.
First, let’s acknowledge that you’ve already taken a massive step by creating your own network with nodes and connections. That’s no easy feat! Now, to train your network, you need to understand how back propagation works. Essentially, it’s an algorithm that helps your network learn from its mistakes.
Imagine you’re trying to recognize handwritten digits (like in the MNIST dataset). Your network makes a prediction, but it’s not accurate. Back propagation kicks in to calculate the error, and then it adjusts the connection weights to minimize that error. This process repeats until your network becomes proficient in recognizing those digits.
Here’s a simple way to think about it: imagine water flowing through a pipe. The water represents the error, and the pipe represents the connections between nodes. Back propagation is like tracing the path of the water backwards to find the source of the error, and then adjusting the pipe’s shape to reduce the error.
If you’re still struggling to visualize it, I recommend watching 3Blue1Brown’s animated explanation of back propagation on YouTube. It’s an excellent resource that’ll help you understand the concept more intuitively.
Remember, learning back propagation takes time and practice. Be patient, and don’t be afraid to ask for help. You got this!