答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's paramet...
There are some algorithms that can automatically tell you if gradient descent has converged, but you must define a threshold for the convergence beforehand, which is also pretty hard to estimate. For this reason, simple plots are the preferred convergence test. ...
Before we dive into gradient descent, it may help to review some concepts from linear regression. You may recall the following formula for the slope of a line, which is y = mx + b, wheremrepresents the slope andbis the intercept on the y-axis. You may also recall plotting a scatterpl...
题目 题目: What is the significance of 'gradient descent' in training AI models? 答案 解析 null 本题来源 题目:题目: What is the significance of 'gradient descent' in training AI models? 来源: 模拟ai英文面试题目及答案 收藏 反馈 分享
Gradient ascent works in the same manner as gradient descent, with one difference.The task it fulfills isn’t minimization, but rather maximization of some function. The reason for the difference is that, at times, we may want to reach the maximum, not the minimum of some function; this ...
Here, the term “stochastic” comes from the fact that the gradient based on a single training sample is a “stochastic approximation” of the “true” cost gradient. Due to its stochastic nature, the path towards the global cost minimum is not “direct” as in Gradient Descent, but may ...
Gradient descent is an optimization algorithm that refines a machine learning model's parameters to create a more accurate model. The goal is to reduce a model's error or cost function when testing against an input variable and the expected result. It's calledgradientbecause it is analogous to...
Further, you plan to use both feature scaling (dividing by the "max-min", or range, of a feature) and mean normalization.What is the normalized feature x(2)2? (Hint: midterm = 89, final = 96 is training example 1.) Please round off your answer to two decimal places and enter in...
This is the mathematical notation of a gradient of a function with an x and y dimension. The gradient is a vector made of the partial derivatives of the function with respect to each input to the function, represented as a vector. True gradients are both more accurate, and faster to compu...
Optimal fitting is usually guaranteedMost machine learning models use gradient descent to fit models, which involves tuning the gradient descent algorithm and provides no guarantee that an optimal solution will be found. By contrast, linear regression that uses the sum of squares as a cost function...