**Full Changelog**: https://github.com/raminmohammadi/GradientBlueprint/compare/v.1.0.0...v1.0.1
v.1.0.0
I'm thrilled to announce the inaugural release of Gradient Toolkit (Version 1.0), 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.
Key Features and Highlights:
* __Variable Class__:
Central to the Gradient Toolkit is the Variable class, a symbolic representation of variables that facilitates efficient computation of gradients. Users can create symbolic variables, perform arithmetic operations on them, and compute gradients effortlessly, gaining deeper insights into the mechanics of gradient-based optimization.
* __Cost Functions__:
The toolkit provides a rich collection of cost functions commonly used in machine learning, including mean squared error (MSE), cross-entropy loss, hinge loss, and more. These cost functions leverage the Variable class to compute gradients, enabling users to optimize model parameters effectively during training.
* __Optimization Algorithms__:
Gradient descent variants such as stochastic gradient descent (SGD) and batch gradient descent are implemented within the toolkit. Users can seamlessly apply these optimization algorithms to minimize cost functions and update model parameters iteratively, driving model convergence and improving performance.
* __Neural Networks (NNs)__:
Leveraging the power of the Variable class, Gradient Toolkit offers support for building and training neural networks. Users can construct multi-layer perceptron architectures, customize activation functions, and optimize network parameters using gradient-based techniques, empowering them to create sophisticated neural models for various tasks.
* __Logistic Regression (LR)__:
Gradient Toolkit includes utilities for logistic regression, a fundamental binary classification algorithm. With built-in support for logistic regression models, users can train classifiers, compute probabilities, and optimize model parameters using gradient descent, enhancing their understanding of logistic regression principles.
* __Support Vector Machines (SVMs)__:
The toolkit extends its capabilities to support vector machines (SVMs), a powerful class of supervised learning algorithms. Users can train SVM models using both primal and dual formulations, apply kernel methods for nonlinear classification, and optimize model parameters efficiently using gradient-based techniques.
How to Get Started:
To start exploring Gradient Toolkit (Version 1.0), simply install the package using your preferred package manager:
pip install autograd==1.0
Visit our documentation for detailed usage instructions, examples, and API references to help you make the most of the toolkit's capabilities.
Feedback and Contributions:
We value your feedback and contributions! If you encounter any issues, have suggestions for improvements, or would like to contribute to the development of Gradient Toolkit, please visit our GitHub repository. Together, we can build a vibrant community dedicated to advancing gradient-based optimization techniques and machine learning principles.
Thank you for joining us on this exciting journey with Gradient Toolkit. We can't wait to see the incredible machine learning applications you'll create!