PyTorch Explained: Machine Learning You Can Watch Learn
TensorFlow showed one way to train a model, with layers stacked and compiled. PyTorch, released by Meta's AI research lab in 2016, takes a more hands-on approach: you write ordinary Python, tensors track their own gradients, and the training loop is a loop you can read, print from, and change. That
Posted on Nov 1 • Originally published at practicaldevblog.blogspot.com
TensorFlow showed one way to train a model, with layers stacked and compiled. PyTorch, released by Meta's AI research lab in 2016, takes a more hands-on approach: you write ordinary Python, tensors track their own gradients, and the training loop is a loop you can read, print from, and change. That openness made it the favourite of researchers, and it is now just as common in production, from image models to language models.
Tensors, autograd, and the training loop
PyTorch rests on a few distinctive ideas:
A tiny example shows the shape, a model that learns the line y = 2x + 1 from four examples:
import torch import torch.nn as nn xs = torch. tensor ([[ 0.0 ], [ 1.0 ], [ 2.0 ], [ 3.0 ]]) ys = torch. tensor ([[ 1.0 ], [ 3.0 ], [ 5.0 ], [ 7.0 ]]) model = nn. Linear ( 1, 1 ) optimizer = torch. optim. SGD ( model. parameters (), lr = 0.01 ) loss_fn = nn.
MSELoss () for _ in range ( 200 ): optimizer. zero_grad () loss = loss_fn ( model ( xs ), ys ) loss. backward () optimizer. step () print ( model ( torch. tensor ([[ 4.0 ]]))) Enter fullscreen mode Exit fullscreen mode nn.Linear(1, 1) creates one input neuron connected to one output, zero_grad clears the previous step's gradients so they do not pile up, loss.backward computes fresh gradients, and step nudges the weights a little in the direction that lowers the loss. After 200 passes, predicting for 4.0 lands near 9.0, not exactly, because training improves gradually rather than solving the equation outright.
PyTorch trades hand-holding for control: the training loop is yours to write, which means yours to debug, and moving a model into production takes extra tooling compared with a more packaged framework. When to reach for it: when you want to understand, customise, or research a neural network; a higher-level library when the model is standard and speed of setup matters more than control.
PyTorch is machine learning you can watch learn: tensors, automatic gradients, and a training loop written out in plain Python. It trades convenience for control over every step, and for anyone who wants to see how a model actually improves, that openness is the point.
Originally published at https://practicaldevblog.blogspot.com/2026/11/pytorch-explained-machine-learning-you.html