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This is a comprehensive package designed to demystify the concepts of gradients and their applications across various machine learning algorithms. With this release, developers and researchers gain access to a powerful toolkit that simplifies the understanding and implementation of gradient-based optimization techniques.
A comprehensive Python library for gradient-based learning, including neural networks, logistic regression, support vector machines (SVM), and more.
The Gradient-Based Learning Library is a versatile toolset for implementing and experimenting with various machine learning algorithms that rely on gradient descent optimization. From basic linear regression to complex neural networks, this library provides a unified interface for building, training, and evaluating models.
Install the library using pip:
pip install gradientblueprint
To use the library, import the necessary modules and classes into your Python code:
from gradientblueprint import Variable, MLP, LogisticRegression, SVM, Optimizer, CostFunction
Then, create instances of the provided classes and customize them according to your requirements. Here's a basic example of building and training a neural network:
# Define input dimension and layer dimensions
input_dim = 10
layer_dims = [32, 16, 8]
# Create a multilayer perceptron (MLP)
mlp = MLP(input_dim=input_dim, layers_dim=layer_dims, activations=['relu', 'relu', 'sigmoid'])
# Define optimizer and cost function
optimizer = Optimizer()
cost_function = CostFunction.mean_squared_error
# Train the MLP
for epoch in range(num_epochs):
# Perform forward pass
# Perform backward pass and update weights using optimizer
Refer to the documentation and examples for detailed usage instructions and customization options.
Explore the examples directory for detailed usage examples and tutorials on how to use different components of the library:
For detailed documentation, including API reference and usage guidelines, refer to the Documentation.
Contributions to the Gradient-Based Learning Library are welcome! If you find any issues or have suggestions for improvement, please submit a pull request or open an issue on GitHub.
Please read the Contributing Guide for details on how to contribute to this project.
This project is licensed under the MIT License.
FAQs
This is a comprehensive package designed to demystify the concepts of gradients and their applications across various machine learning algorithms. With this release, developers and researchers gain access to a powerful toolkit that simplifies the understanding and implementation of gradient-based optimization techniques.
We found that gradientblueprint demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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