@ChatGPT 翻译：
Implement proper authentication and authorization on various endpoint that your website exposes.
Define properly what operations are performed on the client and what operations are performed on the server side.
Check regularly for vulnerable libraries that were used to build your web-site.

@ChatGPT 翻译：Web developers often leaves a lot of loopholes in their websites which adversaries exploit. Some such loopholes can be:

Not performing proper checks on API before sending secrets or authorized data.

Performing critical operations on the client side.

@ChatGPT 网站加载缓慢是一个Web开发中经常出现的错误，请你给我介绍一下，并指出避免措施。请注意内容一定要严谨正确，如果你不知道，请不要乱说！

@ChatGPT 导航栏设计不合理是一个Web开发中经常出现的错误，请你给我介绍一下，并指出避免措施。请注意内容一定要严谨正确，如果你不知道，请不要乱说！

@ChatGPT 对输入框缺少验证是一个Web开发中经常出现的错误，请你给我介绍一下，并指出避免措施。请注意内容一定要严谨正确，如果你不知道，请不要乱说！

@ChatGPT 网站未针对不同分辨率的屏幕进行优化是一个Web开发中经常出现的错误，请你给我介绍一下，并指出避免措施。请注意内容一定要严谨正确，如果你不知道，请不要乱说！

@ChatGPT 网站未针对不同分辨率的屏幕进行优化是一个Web开发中经常出现的错误，请你给我介绍一下，并指出避免措施。请注意内容一定要严谨正确，如果你不知道，请不要乱说！

@ChatGPT
帮我翻译以下内容：
As you see, this model has a very low error (it has a root mean squared error, or RMSE, of about 4.93). In other terms, it has very low bias. However, this model also has a very high variance. After reading the article about bias and variance, we can say that this model is overfit.

This becomes even clearer when we split our dataset into a training portion and a testing portion. We still use our overfit model, but this time we train it only on the training data and then evaluate its performance both on the train set as well as the test set. This allows us to reason about the variance of this particular model. Take a look at the following plot:

@ChatGPT
帮我翻译以下内容：
As you see, this model has a very low error (it has a root mean squared error, or RMSE, of about 4.93). In other terms, it has very low bias. However, this model also has a very high variance. After reading the article about bias and variance, we can say that this model is overfit.

This becomes even clearer when we split our dataset into a training portion and a testing portion. We still use our overfit model, but this time we train it only on the training data and then evaluate its performance both on the train set as well as the test set. This allows us to reason about the variance of this particular model. Take a look at the following plot:

@ChatGPT 帮我翻译：
We now use polynomial regression to predict the number of points achieved based on the number of hours studied. Now we might choose to use a very complicated model and see how it performs. As an example, here’s how a polynomial regression with a degree of 15 looks like:

@ChatGPT
帮我翻译下面这段话：
Training and Testing
Now, why do we even need to split our dataset? Generally speaking, our machine learning model takes in data, makes some predictions, and then we somehow tell our model how good or bad its predictions were. Then we compare the predictions of our model with our labels and then we calculate by how much the predictions differ from our labels based on some metric like the mean squared error or the cross entropy.

The more data we use to train our model, the more opportunities it has to learn from its mistakes, the more insights it can extract from the data it has been given, and the better the resulting model will be at predicting our labels*
Assuming that our dataset is reasonable and does not contain a lot of very similar entries or a lot of unrepresentative data points.
. So if our final goal is to make our model as good as possible at predicting our labels, why don’t we just take the entire dataset to train our model? In theory, if we take the entire dataset to train our model, it will perform better than if we just use 70% or 80% of the data for training. The problem is that if we use all the data for training, we can no longer evaluate the true performance of our model in an unbiased fashion. Sure, we can evaluate the model on the data that it was trained on, but this would be problematic. To understand why, let’s look at a practical example.

ChatGPT 翻译能力测试

@ChatGPT “晚上七点，华灯初上，车水马龙，我一个人，望着这座城市”请对这段内容进行扩充，并增添所处环境、衣着、温度、声音等细节。

@ChatGPT 李白如果见到现代的高铁，他会为高铁作什么诗？

@ChatGPT 我要给导师写一封邮件，请他帮我修改论文，要表达出对老师的尊敬，字数在150字。

@ChatGPT 我要给导师写一封邮件，请他帮我修改论文，要表达出对老师的尊敬，字数在150字。

@ChatGPT 怎么准备六级英语考试的听力题

@ChatGPT 如果大量中国居民不断在英国说中式英语，长此以往，英国人的口音和语法会不会变成中式英语？

@ChatGPT 我认为当下中国教育体制仍旧存在许多亟待改进的问题，例如德育的缺失、对想象力的扼杀、对个性的压制等，你认为还存在哪些问题，又该如何解决呢？