[Term Entry] Neural Networks ai gradient descent #5785
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PragatiVerma18
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mamtawardhani
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status: under review
mamtawardhani
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Dec 16, 2024
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Hey @PragatiVerma18 , thank you for contributing to Codecademy Docs, the entry is nicely written! 😄
I've suggested a few changes, could you please review and modify those at your earliest convenience? Thank you! 😃
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| Title: 'Gradient Descent' | |||
| Description: 'Gradient Descent is an optimization algorithm used in machine learning and neural networks to minimize a cost function by iteratively moving towards the minimum using the gradient of the function.' | |||
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| **Gradient Descent** is an optimization algorithm commonly used to minimize a cost function in machine learning and neural networks. The goal of gradient descent is to find the optimal parameters (weights) for a model that minimizes the error or loss function. |
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| **Gradient Descent** is an optimization algorithm commonly used to minimize a cost function in machine learning and neural networks. The goal of gradient descent is to find the optimal parameters (weights) for a model that minimizes the error or loss function. | |
| **Gradient Descent** is an optimization algorithm commonly used in machine learning and neural networks to minimize a cost function. Its goal is to iteratively find the optimal parameters (weights) that minimize the error or loss. |
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| **Gradient Descent** is an optimization algorithm commonly used to minimize a cost function in machine learning and neural networks. The goal of gradient descent is to find the optimal parameters (weights) for a model that minimizes the error or loss function. | ||
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| In the context of neural networks, gradient descent adjusts the model’s parameters by computing the gradient (or derivative) of the cost function with respect to each parameter. The algorithm then updates the parameters in the direction that reduces the cost. |
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| In the context of neural networks, gradient descent adjusts the model’s parameters by computing the gradient (or derivative) of the cost function with respect to each parameter. The algorithm then updates the parameters in the direction that reduces the cost. | |
| In neural networks, gradient descent computes the gradient (derivative) of the cost function with respect to each parameter. It then updates the parameters in the direction of the negative gradient, effectively reducing the cost with each step. |
| | Type | Description | | ||
| | ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ||
| | **Batch Gradient Descent** | Uses the entire dataset to compute the gradient and update the weights. Typically slower but more accurate for large datasets. | | ||
| | **Stochastic Gradient Descent (SGD)** | Uses a single sample to compute the gradient and update the weights. Faster, but the updates are noisy and can lead to fluctuations in the convergence path. | |
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| | **Stochastic Gradient Descent (SGD)** | Uses a single sample to compute the gradient and update the weights. Faster, but the updates are noisy and can lead to fluctuations in the convergence path. | | |
| | **Stochastic Gradient Descent (SGD)** | Uses a single sample to compute the gradient and update the weights. It is faster, but the updates are noisy and can cause fluctuations in the convergence path. | |
| theta = theta - learning_rate * gradient_of_cost_function | ||
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| - `theta`: The parameter (weight) of the model. |
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| - `theta`: The parameter (weight) of the model. | |
| - `theta`: The parameter (weight) of the model that is being optimized. |
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| - `theta`: The parameter (weight) of the model. | ||
| - `learning_rate`: A hyperparameter that controls the step size. | ||
| - `gradient_of_cost_function`: The gradient (derivative) of the cost function with respect to the parameters. |
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| - `gradient_of_cost_function`: The gradient (derivative) of the cost function with respect to the parameters. | |
| - `gradient_of_cost_function`: The gradient (derivative) of the cost function with respect to the parameters, indicating the direction and magnitude of the change needed. |
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| theta = initial_value # Parameters (weights) | ||
| learning_rate = value # Learning rate |
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| theta = initial_value # Parameters (weights) | |
| learning_rate = value # Learning rate | |
| theta = initial_value # Model Parameters (weights) | |
| learning_rate = value # Learning rate (step size) |
| # Repeat until convergence | ||
| for i in range(iterations): | ||
| # Calculate the gradient of the cost function | ||
| gradient = compute_gradient(X, y, theta) |
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| gradient = compute_gradient(X, y, theta) | |
| gradient = compute_gradient(X, y, theta) # Gradient calculation |
| # Cost function (Mean Squared Error) | ||
| def compute_cost(X, y, theta): | ||
| m = len(y) | ||
| cost = (1/(2*m)) * np.sum((X*theta - y)**2) |
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| cost = (1/(2*m)) * np.sum((X*theta - y)**2) | |
| cost = (1/(2*m)) * np.sum((X*theta - y)**2) # The cost function for linear regression |
mamtawardhani
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status: review 1️⃣ completed
status: waiting for author
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status: under review
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Hey @mamtawardhani, I am done with the changes. Please review. Thanks. 🫡 |
mamtawardhani
approved these changes
Dec 18, 2024
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Thank you for contributing to Codecademy Docs @PragatiVerma18 😄
The entry looks good to be merged! 🚀
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👋 @PragatiVerma18 🎉 Your contribution(s) can be seen here: https://www.codecademy.com/resources/docs/ai/neural-networks/gradient-descent Please note it may take a little while for changes to become visible. |
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Description
Add a new term entry in the AI concept for neural-network: gradient descent.
Issue Solved
Closes #4526
Type of Change
Checklist
mainbranch.Issues Solvedsection.