For far too long, a neural network was just a magical black box to me. How could it so accurately predict answers to so many different problems? As a developer, I adopted neural networks. I wasn’t born into them or molded by them like a data scientist. I never understood why we needed activation functions, why biases mattered, or why adding more layers could outperform wider ones. Until it clicked. And suddenly, neural networks became simple. In this session, we’ll peel back the mystery and build an intuitive understanding of what a neural network really does. With just a bit of math and a few clear mental models, you’ll finally see why these components matter and how they fit together. If you’ve ever nodded along while secretly thinking “I still don’t get it,” this session is for you.