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Learn PyTorch

Tensors, autograd and the training loop you write yourself.

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PyTorch is an open-source deep learning framework for Python, widely used in research and increasingly in production. It feels like NumPy with two superpowers: tensors that run on a GPU and automatic differentiation through autograd. Instead of hiding training behind one call, PyTorch has you write the loop yourself, forward pass, loss, backward, optimizer step, which makes it a good teacher of how neural networks actually learn. Beginners move from tensors to nn.Module, Dataset and DataLoader, then to real image or text models. Learnr builds a PyTorch course around your experience, explaining each piece in short lessons with runnable code.

How to learn PyTorch, step by step

Get fluent with tensors first. Create tensors, check shape and dtype, reshape with view and permute, and move them with .to(device). Most PyTorch bugs are shape mismatches, so printing shapes at every step is the habit that saves the most time.
Watch autograd work on a tiny function. Make a tensor with requires_grad=True, compute a simple expression, call backward and read .grad. Then fit a line with manual gradient descent so you understand what an optimizer does before you use one.
Write the training loop by hand. Subclass nn.Module, pick a loss such as CrossEntropyLoss and an optimizer such as Adam, then loop: zero_grad, forward, loss, backward, step. Train on MNIST or FashionMNIST and log loss per epoch.
Separate training and evaluation properly. Call model.eval() and wrap validation in torch.no_grad() so dropout and batch norm behave correctly and memory stays low. Track validation accuracy each epoch to see overfitting as it happens.
Build a real dataset pipeline. Write a custom Dataset with __len__ and __getitem__ for your own images or text, add transforms from torchvision, and batch it with a shuffled DataLoader. Learnr lessons in short daily doses help these APIs stick between projects.
Fine-tune a pretrained model. Load a pretrained ResNet from torchvision, replace the final layer for your classes and train only the head first. Save the state_dict and reload it to confirm you can reproduce your results later.

What you'll learn

Create, reshape and move tensors between CPU and GPU
Use autograd to compute gradients and understand requires_grad
Define models by subclassing nn.Module
Write a complete training and evaluation loop with an optimizer
Load data efficiently with Dataset and DataLoader and save trained weights

What's inside your PyTorch course

A sample path Learnr builds for PyTorch. Your real course adapts to your goal and level.

1. Tensors, dtypes and devices
2. Autograd and gradients
3. Building models with nn.Module
4. Losses, optimizers and the training loop
5. Datasets, DataLoaders and transforms
6. CNNs, transfer learning and saving models

How long does it take to learn PyTorch?

If you already know Python and basic machine learning, the core of PyTorch takes about 3 to 6 weeks at 5 hours a week. Comfort building and debugging your own architectures typically takes 2 to 4 months of project work.

What you need before you start

Solid Python and NumPy, plus an understanding of what a neural network, loss function and gradient descent are. A GPU helps but free notebook environments are enough to start.

Common mistakes to avoid

Forgetting optimizer.zero_grad(), so gradients from previous batches keep accumulating and training behaves strangely.
Leaving the model in training mode during evaluation, which keeps dropout active and makes validation scores noisy and too low.
Mixing devices, such as a model on the GPU and inputs on the CPU. Move both with the same device variable at the start.

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PyTorch questions, answered

Should I learn PyTorch or TensorFlow?

Both are capable. PyTorch is very common in research and in many recent tutorials and open-source models, while TensorFlow with Keras is common in some production and mobile settings. The concepts transfer, so pick one and finish a project.

Do I need a GPU to learn PyTorch?

No. Small models on MNIST train fine on a CPU, and free hosted notebooks with GPU access exist for bigger experiments. You only need serious hardware for large models.