Introduction to PyTorch
You built the engine from pistons and crankshafts. Now learn the one everyone actually drives. Build and train neural networks using PyTorch's nn.Module, nn.Sequential, and autograd. Use PyTorch tensors, GPU acceleration, and the standard training loop (zerograd, forward, loss, backward, step). Convert your from-scratch mini framework components to their PyTorch equivalents. Profile and compare training speed between your pure-Python framework and PyTorch on the same task. You have a working mini framework. Linear layers, ReLU, dropout, batch norm, Adam, a DataLoader, a training loop. It trains a 4-layer network on a circle classification problem in pure Python. It is also 500x slower than PyTorch on the same problem. Your mini framework processes one sample at a time with nested Python loops. PyTorch dispatches the same operations to optimized C++/CUDA kernels that run on GPU. On a single NVIDIA A100, PyTorch trains a ResNet-50 (25.6M parameters) on ImageNet (1.28M images) in about 6 hours. Your framework would take roughly 3,000 hours on the same task -- if it didn't run out of memory first. Speed is not the only gap. Your framework has no GPU support. No automatic differentiation -- you hand-wrote backward() for every module. No serialization. No distributed training. No mixed precision. No way to debug gradient flow without print statements. PyTorch fills every one of these gaps. And…
Introduction to PyTorch: You built the engine from pistons and crankshafts. Now learn the one everyone actually drives. Build and train neural networks using…
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