# What is B in gradient descent?

Now let's run gradient descent using our new cost function. There are two parameters in our cost function we can control: m (weight) and b (bias).

## What is gradient descent rule?

Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. Training data helps these models learn over time, and the cost function within gradient descent specifically acts as a barometer, gauging its accuracy with each iteration of parameter updates.

## What is the formula of gradient descent?

Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. let's consider a linear model, Y_pred= B0+B1(x). In this equation, Y_pred represents the output. B0 is the intercept and B1 is the slope whereas x is the input value.

## What is M and C in gradient descent?

m: Slope of the line (For a unit increase in the quantity of X, Y increases by m. 1 = m units.) c: y intercept (The value of Y is c when the value of X is 0)

## What is J in gradient descent?

Gradient descent is used to minimize a cost function J(W) parameterized by a model parameters W. The gradient (or derivative) tells us the incline or slope of the cost function. Hence, to minimize the cost function, we move in the direction opposite to the gradient.

## What is Alpha in gradient descent?

Selecting a learning rate

Notice that for a small alpha like 0.01, the cost function decreases slowly, which means slow convergence during gradient descent. Also, notice that while alpha=1.3 is the largest learning rate, alpha=1.0 has a faster convergence.

## What is Theta in deep learning?

Theta is the weight of your function. It can be initialized in various ways, in general it is randomized. After that, the training data is used to find the most accurate value of theta. Then you can feed new data to your function and it will use the training value of theta to make a prediction.

## What is local minima in gradient descent?

Ans: Local minima:

The point in a curve which is minimum when compared to its preceding and succeeding points is called local minima.

## How do we calculate gradient?

How to calculate the gradient of a line
1. Select two points on the line that occur on the corners of two grid squares.
2. Sketch a right angle triangle and label the change in y and the change in x .
3. Divide the change in y by the change in x to find m .

To achieve this goal, it performs two steps iteratively:
1. Compute the gradient (slope), the first order derivative of the function at that point.
2. Make a step (move) in the direction opposite to the gradient, opposite direction of slope increase from the current point by alpha times the gradient at that point.

## What is SGD ML?

Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique. Stochastic gradient descent is widely used in machine learning applications.

## What is step size in neural network?

The amount that the weights are updated during training is referred to as the step size or the “learning rate.” Specifically, the learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0.

## What is gradient descent and Delta Rule?

Gradient descent is a way to find a minimum in a high-dimensional space. You go in direction of the steepest descent. The delta rule is an update rule for single layer perceptrons. It makes use of gradient descent.

A typical problem for both local minima and saddle-points is that they are often surrounded by plateaus of small curvature in the error. While gradient descent dynamics are repelled away from a saddle point to lower error by following directions of negative curvature, this repulsion can occur slowly due to the plateau.

## What is loss function in gradient descent?

Gradient descent is an iterative optimization algorithm used in machine learning to minimize a loss function. The loss function describes how well the model will perform given the current set of parameters (weights and biases), and gradient descent is used to find the best set of parameters.

## How do you select learning rate in gradient descent?

How to Choose an Optimal Learning Rate for Gradient Descent
1. Choose a Fixed Learning Rate. The standard gradient descent procedure uses a fixed learning rate (e.g. 0.01) that is determined by trial and error. ...
2. Use Learning Rate Annealing. ...
3. Use Cyclical Learning Rates. ...
4. Use an Adaptive Learning Rate. ...
5. References.

## What is Y MX B?

Y = mx + b is the slope-intercept form of the equation of a straight line. In the equation y = mx + b, m is the slope of the line and b is the intercept. X and y represent the distance of the line from x-axis and y-axis, respectively. The value of b is equal to y when x = 0, and m shows how steep the line is.

## What is global and local minima?

A local minimum of a function is a point where the function value is smaller than at nearby points, but possibly greater than at a distant point. A global minimum is a point where the function value is smaller than at all other feasible points.

## What is local and global maxima and minima?

A maximum or minimum is said to be local if it is the largest or smallest value of the function, respectively, within a given range. However, a maximum or minimum is said to be global if it is the largest or smallest value of the function, respectively, on the entire domain of a function.

## What are local minima?

Local minimum is the point in the domain of the functions, which has the minimum value. The local minimum can be computed by finding the derivative of the function. The first derivative test, and the second derivative test, are the two important methods of finding the local minimum for a function.

## What are theta 0 and theta 1?

Here theta-0 and theta-1 represent the parameters of the regression line. In the line equation ( y = mx + c ), m is a slope and c is the y-intercept of the line. In the given equation, theta-0 is the y-intercept and theta-1 is the slope of the regression line.

## What is J in machine learning?

What is cost function: The cost function “J( θ01)” is used to measure how good a fit (measure the accuracy of hypothesis function) a line is to the data. If the line is a good fit, then your predictions will be far better.

## Why is cost divided by 2m?

Dividing by 2m ensures that the cost function doesn't depend on the number of elements in the training set. This allows a better comparison across models.
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