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卷积：卷积核在像素上滑动

了解滤波器如何检测边缘与纹理

卷积：卷积核在像素上滑动 是 CoddyKit 上的免费 Deep Learning Academy 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

An Image Is Just Numbers

To a network, a photo is a grid of pixel values. A small grayscale image might be 28x28 numbers, each from 0 (black) to 255 (white).

Why Not Just Flatten It?

Flattening an image into one long vector throws away the spatial layout. Pixels near each other matter, and a plain dense layer ignores that.

Meet the Kernel

A kernel is a tiny grid of weights, often 3x3. It slides across the image, looking at one small patch at a time.

The Sliding Window

At each position the kernel covers a patch, multiplies overlapping values, and sums them into one number. Then it slides over and repeats. 🔍

That Sum Is the Output Pixel

Each weighted sum becomes a single pixel in the result. Slide across the whole image and you build a fresh grid called a feature map.

Kernels Detect Patterns

A well-tuned kernel lights up where its pattern appears. One kernel might fire on vertical edges, another on bright corners or smooth textures.

An Edge Detector by Hand

This classic 3x3 kernel responds strongly to vertical edges, where light meets dark across a column.

edge_kernel = [
    [-1, 0, 1],
    [-1, 0, 1],
    [-1, 0, 1],
]

The Weights Are Learned

You do not hand-pick kernel numbers. Training adjusts them so each filter learns whatever pattern helps the network classify images.

Same Kernel, Everywhere

One kernel reuses the same weights across the whole image. This weight sharing means far fewer parameters than a dense layer would need.

A Conv Layer in PyTorch

nn.Conv2d wraps all of this. Here a Conv2d takes 1 input channel and learns 8 different 3x3 kernels.

import torch.nn as nn
conv = nn.Conv2d(1, 8, kernel_size=3)

Many Kernels, Many Maps

That layer learns 8 kernels, so it outputs 8 feature maps. Each one highlights a different pattern the network found useful.

Quick Check

Let us check what a convolution kernel actually produces.

Recap: Kernels Slide

You learned that a kernel slides over pixels, summing weighted patches into a feature map. Its weights are learned, shared, and tuned to spot edges and textures. 🎉

常见问题解答

「卷积：卷积核在像素上滑动」课时是免费的吗？

是的 — 「卷积：卷积核在像素上滑动」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。

「卷积：卷积核在像素上滑动」这节课中我会学到什么？

了解滤波器如何检测边缘与纹理你通过在浏览器中直接运行的动手代码来练习 Deep Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Deep Learning Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Deep Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「卷积：卷积核在像素上滑动」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Deep Learning Academy 课中编写并运行代码吗？

能。每节 Deep Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

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