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Name: ___ Number:Gradients Reviewed: ___ Date: ___Directions: Use the formula for gradient to complete the following problems. Show all your work. Round all answers to the nearest hundredths. Circle
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To fill out the gradient descent algorithm, follow these steps:
02
Initialize the values of the model parameters randomly or with some pre-defined values.
03
Compute the predicted output of the model for the given input data.
04
Calculate the cost or error between the predicted output and the actual output.
05
Update the values of the model parameters by taking a small step in the opposite direction of the gradient of the cost function.
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Repeat steps 2-4 until the cost function converges to a minimum or a specified number of iterations are reached.

Who needs gradient descent algorithm and?

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The gradient descent algorithm is used in various fields and scenarios such as:
02
- Machine learning: It is widely used in training models and optimizing the parameters of machine learning algorithms.
03
- Deep learning: It is a fundamental optimization algorithm for training deep neural networks.
04
- Data analysis: It helps in finding the optimal values of parameters in regression, classification, and clustering models.
05
- Optimization problems: It can be used to solve optimization problems by finding the minimum or maximum of a function.
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- Mathematics: It is used in numerical optimization to find the critical points of functions.
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- Engineering: It is used in various engineering fields for optimization and parameter estimation.
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Gradient descent is an optimization algorithm used to minimize the error of a model by adjusting its parameters.
Data scientists and machine learning engineers are required to use the gradient descent algorithm in their model optimization process.
Gradient descent algorithm is implemented through iterative updates of model parameters based on the gradient of the loss function.
The purpose of gradient descent algorithm is to find the optimal parameters of a model that minimize the error or loss function.
The information reported on gradient descent algorithm includes the initial parameters of the model, learning rate, and convergence criteria.
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