PyTorch Without the Calculus: What It Is and What You Can Ignore
A gentle, math-free introduction to PyTorch for engineers and technical PMs — what it actually is, what you can safely skip, and how to set up Google Colab to run real AI models.
Practical PyTorch: Running Models — Foundations
Tensors, shapes, the GPU, and building your first model from a single layer up — the math-free groundwork, in Colab.
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Tensors: The One Data Structure You Actually Have to Like
A beginner-friendly tour of pytorch tensors — creating them, indexing and reshaping them, and doing arithmetic on them — with no linear-algebra homework attached.
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Tensors on the Move: GPUs, NumPy, and Back
A beginner-friendly guide to moving PyTorch tensors between the CPU and GPU with .to(device), trading data with NumPy, and pulling a single value out with .item() — no math required.
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Reading Tensor Shapes Without Panicking
A practical PyTorch shape-reading guide for beginners: batches, image tensors, classifier outputs, token IDs, hidden states, and the print-shape habit that makes model code debuggable.
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What a Model Is Made Of: Build One from a Single Layer Up
A gentle, math-free look at what a neural network is actually made of — layers and parameters — by building one yourself in PyTorch, from a single nn.Linear up to a small stack, and reading its insides. Beginner-friendly.
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From Perceptron to Deep Net: The Famous Little Models You Just Built
A gentle, math-free tour of the models your toy network descends from — the perceptron, linear and logistic regression, the multi-layer perceptron that cracked XOR, and LeNet, the first famous CNN — built in a few lines of PyTorch.
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From Layers to a Live Digit Reader
Your first capstone: take the MLP you built by hand, load trained weights into it, and wrap it in a Gradio app that reads handwritten digits you draw — a real, shareable tool, no training required.