Building Your First Neural Network with PyTorch: From Tensor Operations to Training Loops

You've written distributed systems, optimized database queries, and architected microservices—but when someone mentions "backpropagation" or "gradient descent," there's a nagging gap in your mental model. The math makes sense on paper. Derivatives, chain rules, optimization surfaces—you studied this in university. Yet translating those concepts into working code feels like crossing into foreign territory, one where your hard-won engineering intuition suddenly stops applying. The problem isn't intelligence or mathematical capability. It's that most deep learning frameworks were built by researchers for researchers, prioritizing mathematical elegance over debuggability. When your model refuses to converge, you're left staring at tensor shapes and loss curves, unable to step through the computation the way you'd step through a misbehaving service. PyTorch changes this dynamic fundamentally. It treats neural networks as what they actually are: directed graphs of differentiable operations,