Numba vs Torch: What are the differences?

# Introduction
This markdown provides a comparison between Numba and Torch for website integration.

1. **Execution Model**: Numba is a just-in-time compiler that translates Python functions to optimized machine code, whereas Torch is a deep learning framework that uses computational graphs for automatic differentiation.
2. **Language Support**: Numba works with Python and supports NUMPY libraries for numerical computation, while Torch is primarily used with Lua programming language and has Python bindings for additional functionality.
3. **Application Domain**: Numba is suited for general-purpose numerical computations and accelerating numerical algorithms, while Torch is focused on deep learning tasks such as building and training neural networks.
4. **Community Support**: Numba has a relatively smaller community compared to Torch, which is backed by Facebook AI research and has a larger user base contributing to its development and support.
5. **Ecosystem Integration**: Numba can seamlessly integrate with existing Python libraries and workflows, while Torch has its ecosystem of tools and modules specifically designed for deep learning applications.
6. **Ease of Use**: Numba provides a simpler approach to optimizing Python code with minimal changes, whereas Torch requires a steeper learning curve due to its focus on neural networks and specialized deep learning concepts.

In Summary, this comparison highlights key differences between Numba and Torch in terms of their execution model, language support, application domain, community support, ecosystem integration, and ease of use.