What is PyTorch?
PyTorch is the most widely used deep learning framework in research and increasingly in industry. It gives you tensors (GPU-accelerated arrays), automatic differentiation (autograd), and building blocks for defining and training networks. It is how you turn neural network theory into running models.
Why it matters
You will not implement backpropagation by hand in real work — you will use a framework, and PyTorch is the dominant one. Knowing its core abstractions lets you build, train, and debug models, read the vast PyTorch ecosystem, and follow modern research code, which is almost all PyTorch.
What to learn
- Tensors and moving them to the GPU
- Autograd and the computation graph
nn.Modulefor defining models- Optimizers and the parameter update
- The standard training loop structure
- Datasets and DataLoaders
- Saving and loading model weights
Common pitfall
Forgetting to zero the gradients each step. PyTorch accumulates gradients by
default, so without optimizer.zero_grad() they pile up across iterations and
training goes haywire. The zero-grad, backward, step sequence has a fixed order —
get it wrong and the model silently fails to learn.
Resources
Primary (free):
- PyTorch — Learn the basics · docs
- PyTorch — Tutorials · docs
- Andrej Karpathy — Building makemore · video
Practice
In PyTorch, define a small nn.Module, create some tensors, and run one training
step by hand: forward pass, compute loss, zero gradients, backward, optimizer
step. Move the tensors to a GPU in Colab. Done when you can write the training
step from memory in the right order.
Outcomes
- Create tensors and run them on a GPU.
- Define a model with
nn.Module. - Write the zero-grad, backward, step training sequence correctly.
- Save and load model weights.