gradient的音标是[ˈgrædɪəʊn] ,基本翻译是“斜面;斜坡;斜度;梯度”,速记技巧是:grad=步+e→一步一步走→斜度。
Gradient这个词源于拉丁语gradus,意为“步骤,等级”。它的变化形式主要有名词和形容词两种,名词形式grads或grades,形容词形式gradual。
相关单词:
1. Gradualism:渐进主义,指一种在变化过程中逐渐推进的方法或理论。
2. Gradual:逐渐的,表示变化或进展缓慢。
3. Gradualist:渐进论者,通常指主张在变革过程中逐步推进的人。
4. Graduality:渐进性,指渐进变化的特点或性质。
5. Incremental:递增的,表示变化很小但持续不断。
6. Progressive:进步的,表示在某个领域或过程中不断进步或发展。
7. Evolving:演变的,表示逐渐发展或变化的过程。
8. Metamorphosis:蜕变,通常指逐渐的变化过程。
9. Transformation:转变,通常指从一种状态或形式到另一种状态或形式的转变过程。
10. Modification:修改,通常指逐渐的改变或修正。
这些单词都与gradient这个词有密切的联系,因为它们都表示变化、进展或发展过程,而gradient这个词正是描述这种变化过程的术语。
常用短语:
1. gradient descent 梯度下降
2. gradient field 梯度场
3. gradient vector field 梯度向量场
4. gradient-free optimization 无梯度优化
5. gradient-based optimization 有梯度优化
6. gradient descent algorithm 梯度下降算法
7. gradient descent optimization 梯度下降优化
双语例句:
1. The gradient of the function is used to determine the direction of maximum change.
函数的梯度被用来确定最大变化的方向。
2. The gradient of the market is a measure of its volatility.
市场的梯度是衡量其波动性的一个指标。
3. The gradient of the ocean current determines its direction and speed.
洋流的梯度决定了其方向和速度。
4. The gradient of the weather system determines its movement and intensity.
天气系统的梯度决定了其移动和强度。
5. Gradient descent optimization is commonly used in machine learning algorithms.
梯度下降优化在机器学习算法中经常被使用。
6. The gradient descent algorithm is a popular method for finding local minima in a function.
梯度下降算法是一种在函数中找到局部最小值的一种流行方法。
7. Gradient vector fields are used to visualize the flow of energy in a system.
梯度向量场被用来可视化系统的能量流动。
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.