Part I – Foundations of Image Tensors and Preprocessing¶
“Before we learn to classify, segment, or detect… we must learn to feed the model. And that starts with understanding how neural networks perceive images.”
👁️ Seeing Through the Model’s Eyes¶
Convolutional Neural Networks are brilliant—but only when you talk to them in their native language: tensors. If you feed a CNN the wrong shape, wrong scale, or wrong format, it won’t complain immediately—it will just fail silently, learning the wrong things or nothing at all.
That’s why Part I is all about foundations. Here, we zoom in on the seemingly “simple” steps that trip up most beginners (and even experienced practitioners when switching frameworks).
These first three chapters serve one mission:
To teach you how to guide a raw image into a form that a CNN can understand, process, and learn from—without surprises.
What You’ll Master in This Part¶
What is an image from a neural network’s perspective (beyond what you see in a photo viewer)
Tensor fundamentals—what shapes mean, how memory layout works, and how to reshape, permute, and batch like a pro
The full input pipeline—from disk to tensor to Conv2D-ready data, both in PyTorch and TensorFlow
How to debug common issues like: “shape mismatch,” “expected 3 channels,” or “model outputs garbage”
Chapter Breakdown¶
| Chapter | Title | What You’ll Learn |
|---|---|---|
| 1 | How a Neural Network Sees an Image | JPEG vs raw pixel data, RGB channels, tensor layout differences, visual walkthrough of image → input |
| 2 | What is a Tensor (in Code and in Mind)? | Tensor shapes, dimensionality, reshaping, broadcasting, permute vs transpose |
| 3 | From Pixels to Model Input | Full preprocessing pipeline, float32 conversion, normalization, batching, feeding into Conv2D layers |
Why This Part Matters¶
If your CNN is performing poorly, don’t blame the model yet. Nine times out of ten, it’s not your architecture—it’s your input.
This part will teach you how to:
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Make preprocessing repeatable and reliable
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Build a mental model of how CNNs consume images
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Speak fluently in tensor shapes and formats across frameworks
By the time you finish Part I, you won’t just be “loading images”—you’ll be preparing them for intelligent perception.