Teaching a Model to See: Convolution and LeNet
Why a plain Linear network struggles with images, and how convolution fixes it — a gentle, math-free build-up to LeNet, the first famous CNN, in a few lines of PyTorch.
Practical PyTorch: Running Models — Vision
Convolution and LeNet, ResNet up close, and running a pretrained image classifier end to end — then wrapping it in an app.
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Reading a Real One: ResNet Up Close
Open a famous deep network — ResNet-18 — and read it like a spec sheet: the layer tree, the eleven million parameters, and the named weights, all using the same nn.Module skills you built on a toy. Math-free and beginner-friendly.
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Your First Real Model Run, From Photo to Verdict
A beginner-friendly walkthrough to run a pretrained model in PyTorch end to end — download an image and do image classification with torchvision, no deep-learning math required.
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Logits, Softmax, and the Two Ends of Every Model
A beginner-friendly guide to PyTorch model inputs and outputs — preprocessing and batch dimensions on one end, logits vs probabilities on the other — so you can run any pretrained model with confidence.
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From Notebook to App: Wrap a Model in a UI Anyone Can Use
Build an AI app in PyTorch by wrapping a pretrained model in a Gradio app — a beginner-friendly capstone that turns a model into a real, shareable little tool.