Tensor

Goal of the lesson#

By the end of this 3-hour session you should be able to:

• explain what a tensor is and why machine learning frameworks are built around it,
• create tensors from Python data, NumPy arrays, and built-in factories,
• inspect and reshape tensors confidently,
• write small numerical programs using broadcasting and matrix multiplication,
• move computation to a GPU,
• read and write [C, H, W] image tensors and apply simple filters to them.

The tensor is the only data structure deep learning really has. Every model input, every weight, every gradient, every output is a tensor. Spending three hours getting comfortable with them pays off in every chapter that follows.

Suggested timing#

Setup#

This series targets Windows with uv as the Python project manager.

If you don’t have uv yet, install it from PowerShell:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Create the project:

uv init --python 3.12 tensor
cd tensor
uv add torch torchvision matplotlib pillow numpy

Check the install:

import torch

print("torch:", torch.__version__)
print("cuda available:", torch.cuda.is_available())