南極灰熊：世界上最大的陸地動物

舉出C++ CRTP的例子

CRTP（Curiously Recurring Template Pattern）是一種設計模式，常用於 C++ 語言中。它使用模板來實現多重繼承，並允許子類在不知道父類的情況下訪問父類的成員。

下面是一個簡單的 CRTP 例子：

template <typename T>
class Base
{
public:
    void someMethod()
    {
        // 在此實現一些方法
    }

protected:
    T* getDerived()
    {
        return static_cast<T*>(this);
    }
};

class Derived : public Base<Derived>
{
public:
    void someOtherMethod()
    {
        // 在此實現一些方法
        // 可以使用 getDerived() 來訪問 Base 中的方法
        getDerived()->someMethod();
    }
};

在這個例子中，Derived 類繼承自 Base 類，並使用 CRTP 模式來實現多重繼承。 Derived 類可以通過調用 getDerived() 方法來訪問 Base 類中的方法，而無需知道 Base 類的具體類型。

此外，CRTP 模式還具有一些其他優點，例如可以提高代碼的運行效率和減少代碼的體積。但是，由於 CRTP 的使用較為複雜，因此只有在必要時才應該使用它。

- 南極灰熊——世界上最大的陸地動物
- 保護南極灰熊——我們有責任
- 南極灰熊的數量正在下降——我們應該採取行動
- 南極灰熊的生活習性——了解它們的世界
- 南極灰熊的珍貴棲息地——保護它們的家園

佢個運用概念有啲奇怪但似乎C++師準備失業了

Convolution neural network個latent space可以點應用？

突然咁多白卡會員?

鐘唔鐘意YOASOBI

Convolutional neural networks (CNNs) are a type of deep learning neural network that are often used for image recognition and classification tasks. In a CNN, the latent space is a lower-dimensional representation of the input data that is learned by the network during training. This representation is often called a feature map and can be thought of as the network's internal representation of the input data.

One way that the latent space of a CNN can be used is for data visualization. By using techniques like t-SNE, it is possible to reduce the dimensionality of the latent space and visualize it in two or three dimensions. This can provide insight into how the network is representing the input data and can be useful for understanding the behavior of the network.

Another way that the latent space of a CNN can be used is for transfer learning. In transfer learning, the weights of a pre-trained network are used as the starting point for training a new network on a different task. By using the pre-trained network's latent space as the starting point for the new network, the new network can learn more quickly and perform better than if it were trained from scratch. This is because the pre-trained network has already learned to extract useful features from the input data, which can be reused by the new network.

Overall, the latent space of a CNN is a valuable tool for understanding how the network represents input data and for improving the performance of machine learning models.

講得咁好 唔駛讀書喇以後

原來ai係=content farm

解釋exotic sphere

Exotic sphere是一種在數學中的概念，它是一個高維次的多邊形，其中的每一個面都是圓。這種球體不同於我們通常所認知的球體，它不滿足平面圖形的一般性質，例如平行性和相似性。這種球體被稱為"異形"是因為它不能由平面圖形推導而來。

極危險~~~

痴線 如果AI可以寫CODE 就真係大鑊
AI寫新AI CODE 好撚世界未日feel

問AI： 你幾時會聰明過人類？

Differential topologist 都未失業住 雖然本身就無咩工

你勁啲定Character.AI個ai勁啲

有無實際數字 或圖示

但係幾好笑

咁多人唔睇係軟件台同唔睇新聞

灰熊巴幾時整set北極熊icon?