In this 8-minute PyTorch tutorial, I keep the scope super clear: what PyTorch is, where you’ll actually use it, and the core concepts you need to start solving real machine learning problems. I’ve used TensorFlow/Keras and PyTorch for projects, and I’ll say it bluntly—if your goal is ML engineer / AI engineer / data scientist, PyTorch is a must-have tool in your toolkit. We start with tensors (rank-0 scalar, rank-1 vector, rank-2 matrix), how to create them, check dtype/device, and generate random tensors with torch.randn for quick experiments. Then I explain broadcasting—honestly, it’s a superpower. I show how a scalar gets “stretched” across a vector, and a practical example where you add a per-test curve to a whole matrix of student scores without writing loops. Finally, we build a tiny linear regression in PyTorch on noisy data (y = 2x + noise) and use autograd to learn the slope. The big takeaway: you write normal tensor code, PyTorch builds the computation graph behind the scenes, and loss.backward() walks it backward to fill gradients automatically—exactly the same idea you’ll scale up to millions of parameters in neural nets, attention layers, and LLMs.