gradient的音標是[?gr?d???n] ，基本翻譯是“斜面；斜坡；斜度；梯度”，速記技巧是：grad=步+e→一步一步走→斜度。

Gradient這個詞源于拉丁語gradus，意為“步驟，等級”。它的變化形式主要有名詞和形容詞兩種，名詞形式grads或grades，形容詞形式gradual。

相關單詞：

1. Gradualism：漸進主義，指一種在變化過程中逐漸推進的方法或理論。

2. Gradual：逐漸的，表示變化或進展緩慢。

3. Gradualist：漸進論者，通常指主張在變革過程中逐步推進的人。

4. Graduality：漸進性，指漸進變化的特點或性質。

5. Incremental：遞增的，表示變化很小但持續(xù)不斷。

6. Progressive：進步的，表示在某個領域或過程中不斷進步或發(fā)展。

7. Evolving：演變的，表示逐漸發(fā)展或變化的過程。

8. Metamorphosis：蛻變，通常指逐漸的變化過程。

9. Transformation：轉變，通常指從一種狀態(tài)或形式到另一種狀態(tài)或形式的轉變過程。

10. Modification：修改，通常指逐漸的改變或修正。

這些單詞都與gradient這個詞有密切的聯(lián)系，因為它們都表示變化、進展或發(fā)展過程，而gradient這個詞正是描述這種變化過程的術語。

常用短語：

1. gradient descent 梯度下降

2. gradient field 梯度場

3. gradient vector field 梯度向量場

4. gradient-free optimization 無梯度優(yōu)化

5. gradient-based optimization 有梯度優(yōu)化

6. gradient descent algorithm 梯度下降算法

7. gradient descent optimization 梯度下降優(yōu)化

雙語例句：

1. The gradient of the function is used to determine the direction of maximum change.

函數(shù)的梯度被用來確定最大變化的方向。

2. The gradient of the market is a measure of its volatility.

市場的梯度是衡量其波動性的一個指標。

3. The gradient of the ocean current determines its direction and speed.

洋流的梯度決定了其方向和速度。

4. The gradient of the weather system determines its movement and intensity.

天氣系統(tǒng)的梯度決定了其移動和強度。

5. Gradient descent optimization is commonly used in machine learning algorithms.

梯度下降優(yōu)化在機器學習算法中經(jīng)常被使用。

6. The gradient descent algorithm is a popular method for finding local minima in a function.

梯度下降算法是一種在函數(shù)中找到局部最小值的一種流行方法。

7. Gradient vector fields are used to visualize the flow of energy in a system.

梯度向量場被用來可視化系統(tǒng)的能量流動。

英文小作文：

Gradient Descent: A Simple Optimization Algorithm

Gradient descent is an optimization algorithm that uses the derivative of a function to find the minimum or maximum of that function. It works by iteratively moving in the direction of the negative gradient, which is the derivative of the function with respect to the current position. This simple algorithm has been used for centuries to optimize various problems, from finding the shortest path to optimizing machine learning models.

In this essay, we will explore how gradient descent works and why it is such a powerful tool for optimization. We will also discuss some of its limitations and how to overcome them, as well as its applications in various fields. By understanding gradient descent, we can better appreciate its role in modern machine learning and other areas of artificial intelligence.