This project is a Python-based deep learning library developed for building and training neural networks. The library encompasses core components such as Tensors, Loss Functions, Layers, and Neural Nets. It also integrates Optimizers for efficient model training, implements data handling, and showcases library functionality through practical examples like XOR and FizzBuzz.
- Core components include Tensors, Loss Functions, Layers, and Neural Nets.
- Integration of Optimizers for efficient model training.
- Implementation of data handling for seamless dataset integration.
- Practical examples, including XOR and FizzBuzz, to demonstrate library functionality.
- GPU Acceleration: Incorporate support for GPU acceleration to enhance training speed for large neural networks.
- Additional Activation Functions: Expand the library with a variety of activation functions to provide users with more choices for customizing their neural network architectures.
- Pre-trained Models: Introduce pre-trained models for common tasks (e.g., image classification) to facilitate quick experimentation and transfer learning.
- Visualization Tools: Implement tools for visualizing neural network architectures, training progress, and model performance metrics.
- Distributed Training: Enable distributed training capabilities to scale the training of large models across multiple machines.
- AutoML Integration: Explore integration with AutoML techniques to automate hyperparameter tuning and architecture search.
- ONNX Compatibility: Ensure compatibility with the Open Neural Network Exchange (ONNX) format for enhanced model interoperability with other frameworks.
- Documentation and Tutorials: Develop comprehensive documentation and tutorials to make the library more accessible to users of varying expertise levels.
- Support for Different Frameworks: Extend compatibility to work seamlessly with different deep learning frameworks such as TensorFlow and PyTorch.
- Clone the repository:
git clone https://github.com/ashkaaar/deep-learning-library.git - Navigate into the directory:
cd deep-learning-library - Install the dependencies:
pip install -r requirements.txt - Explore the library functionality through provided examples.
- Import the deep learning library into your Python project.
- Use the library's components to define and train your neural network.
- Explore practical examples like XOR and FizzBuzz to understand library functionality.
- Refer to the documentation for detailed information on each component and feature.