Neograd
Neograd is a deep learning framework developed from scratch using Python and NumPy. It aims to provide a simple yet powerful interface for building and training neural networks. Key features include:
- Customizable Architecture: Users can easily define their own neural network architectures.
- Automatic Differentiation: Facilitates backpropagation and gradient descent optimization.
- Flexible Training: Supports various training techniques and loss functions.
- Lightweight: Designed to be efficient and easy to use, making it suitable for both beginners and experienced practitioners.
Use Cases
- Educational Purposes: Ideal for students and educators looking to understand the fundamentals of deep learning.
- Research: Useful for researchers who want to experiment with novel neural network architectures and training methods.
- Prototyping: Quick prototyping of deep learning models for various applications in computer vision, natural language processing, and more.