用通俗语言理解训练循环
进行预测、衡量误差、调整参数，然后重复
用通俗语言理解训练循环 是 CoddyKit 上的免费 Deep Learning Academy 课时。 这是第 4 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。
Learning Is a Loop
Every deep model learns the same way: a simple loop repeated thousands of times. Master this rhythm and the rest clicks into place. 🔁
Step One Predict
First the model makes a prediction. It takes your input, pushes it through its weights, and guesses an answer, even if that guess is rough at first.
Guesses Start Bad
At the very start the weights are random, so early predictions are basically noise. That is expected, and improving them is the whole point.
Step Two Measure Error
Next you compare the guess to the true answer and compute the loss, a single number showing how wrong the model was this time.
Loss Is the Compass
A high loss means a bad prediction, a low loss means a good one. The model's only goal is to push this number down, step by step.
Step Three Find the Fix
Then the model asks which way to nudge each weight to lower the loss. Those directions are the gradients, computed automatically for you.
Step Four Adjust
Finally the optimizer nudges every weight a tiny bit in the helpful direction. This small update makes the next prediction a little better.
Then Repeat
Predict, measure, adjust, then do it again on the next batch. Across many iterations the loss falls and the model slowly grows accurate.
One Epoch
When the loop has seen every training example once, you have finished one epoch. Most training runs through many epochs in a row.
The Loop in Code
In PyTorch the four steps map to four lines you will write again and again. Here is the core loop in plain code.
pred = model(x)
loss = loss_fn(pred, y)
loss.backward()
optimizer.step()Why It Feels Like Magic
No magic, just repetition. Tiny, guided adjustments over and over turn random weights into a model that genuinely understands your data.
Quick Check
What are the steps of one training loop iteration, in order?
Recap
You learned the core rhythm: predict, measure the loss, then adjust the weights, repeated over many epochs until the model gets good. 🎉
用 AI 导师学习 Python — 免费
在浏览器中编写并运行真实代码，获得全天候 AI 导师的即时帮助，并在网页或应用中继续学习。
- 课程
- 30
- 课程
- 120
常见问题解答
「用通俗语言理解训练循环」课时是免费的吗？
是的 — 「用通俗语言理解训练循环」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。
「用通俗语言理解训练循环」这节课中我会学到什么？
进行预测、衡量误差、调整参数，然后重复 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 4 节课，共 4 节。
「用通俗语言理解训练循环」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
- 人工智能、机器学习与深度学习的比较
- 神经网络为何胜过手工设计的特征
- 深度学习的优势与局限
- 用通俗语言理解训练循环